EVALUATION OF IN SITU DETECTION METHODS FOR …evaluation of in situ detection methods for twospotted spider mites (tetranychus urticae koch) in strawberry by christopher d. crockett

EVALUATION OF IN SITU DETECTION METHODS FOR TWOSPOTTED SPIDER MITES (Tetranychus urticae Koch) IN STRAWBERRY

By

CHRISTOPHER D. CROCKETT

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2015

© 2015 Christopher D. Crockett

To my brilliant and loving parents

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ACKNOWLEDGMENTS

I would like to thank my advisor and major professor, Dr. Oscar Liburd, for giving

me the opportunity to explore the vast field of agricultural entomology, and to pursue my

dream of becoming a research scientist. I thank Dr. Amr Abd-Elrahman for all of his help

and instruction with image analysis and future research goals, Dr. Joseph Noling for his

input on experimental design and sampling, and Dr. Arnold Schumann for all of his

insight on plant imaging methods. I thank all of you, with the greatest sincerity, for

helping to introduce me to an entirely new field in which I hope to leave behind a legacy

of research that will aid the farmers of the world. In addition to my committee members,

I would like to thank Dudley Calfee of Ferris Farms Inc. for all of his help and

cooperation with all of the research that I carried out on their farm. I wouldn’t have been

able to complete this body of research without the support and aid of all of my fellow

members of the Small Fruit and Vegetable IPM Laboratory. I have come to develop

close bonds with all of my fellow labmates, and cherish my experiences with them.

I would like to extend my gratitude to a few of my fellow graduate students and

people that have made my transition to Gainesville, and to graduate school, a truly

enjoyable experience. I thank Matthew Moore, Chase Kimmel, and Michael Bentley for

being some of the best friends and colleagues I have ever had. I would also like to

thank Lindsy Iglesias and Sean McGuire for making me feel like family. You guys have

made Gainesville more than just a place I’ve lived; you’ve made it a place I’ve loved and

felt welcome.

Finally, I would like to thank my family for their unyielding support from so far

away. You have always believed in me, even when I haven’t believed in myself, and I

constantly strive to make all of you proud. Your phone calls and letters gave me

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strength to keep going no matter how busy and stressed I became. To my parents

specifically, you’ve given me so many opportunities that were not afforded to you, and I

am so indescribably grateful for your love and support.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 11

ABSTRACT ................................................................................................................... 12

CHAPTER

1 INTRODUCTION .................................................................................................... 14

2 LITERATURE REVIEW .......................................................................................... 18

Twospotted Spider Mite .......................................................................................... 18 Life Cycle .......................................................................................................... 18 In Field Distribution and Behavior ..................................................................... 19

Current Management Strategies for the Twospotted Spider Mite ..................... 19 Leaf Spectroscopy .................................................................................................. 21

Digital Imagery ........................................................................................................ 22

Study Objectives ..................................................................................................... 22

3 DETECTION AND PREDICTION OF TWOSPOTTED SPIDER MITE (TETRANYCHUS URTICAE KOCH) INFESTATION LEVELS ON STRAWBERRY LEAVES USING VISUAL/NIR SPECTROSCOPY ........................ 24

Introduction ............................................................................................................. 24 Materials and Methods............................................................................................ 26

Strawberry Plants ............................................................................................. 26 Twospotted Spider Mite Colony ........................................................................ 27 Experimental Design and Sampling ................................................................. 27

Leaf Reflectance Spectroscopy ........................................................................ 28

Data Preprocessing and Analysis ..................................................................... 29 Spectra Transformation .................................................................................... 29 Partial Least Squares Regression (PLSR) ....................................................... 29

Results .................................................................................................................... 30 Twospotted Spider Mite Populations ................................................................ 30 PLS Model Development and Prediction .......................................................... 31

Discussion and Conclusions ................................................................................... 33

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4 IN SITU DETECTION OF TWOSPOTTED SPIDER MITE (Tetranych urticae KOCH) INFESTED STRAWBERRY PLANTS USING SIMPLE DIGITAL IMAGERY ............................................................................................................... 45


Imaging Equipment .......................................................................................... 47 20I4 Field Season Image Collection ................................................................. 47 2015 Field Season Image Collection ................................................................ 48

Image Processing and Data Analysis ............................................................... 48 Results .................................................................................................................... 50

20I4 Field Season Image Study ....................................................................... 50 20I5 Field Season Image Study ....................................................................... 50

Discussion and Conclusions ................................................................................... 50

5 DIFFERENCES IN TWOSPOTTED SPIDER MITE POPULATIONS BETWEEN TWO DIFFERENT VARIETIES OF STRAWBERRY, ‘FLORIDA FESTIVAL’ AND ‘ALBION’ AS OBSERVED ON A COMERCIAL FARM ................................... 63


2013-2014 Field Sampling................................................................................ 63

2014-2015 Field Sampling................................................................................ 64 Data Analysis ................................................................................................... 64

Results .................................................................................................................... 64

2013-2014 Study .............................................................................................. 64

2014-2015 Study .............................................................................................. 65 Discussion and Conclusions ................................................................................... 65

6 CONCLUSIONS ..................................................................................................... 71

LIST OF REFERENCES ............................................................................................... 74

BIOGRAPHICAL SKETCH ............................................................................................ 78

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LIST OF TABLES

Table page 4-1 Regression model parameters for single band and vegetation index models

for the 2014 field study. ...................................................................................... 61

4-2 Regression model parameters for single band and vegetation index models for the 2015 field study. ...................................................................................... 62

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LIST OF FIGURES

Figure page 3-1 Greenhouse setup of complete randomized block design of ‘Albion’ and

‘Florida Festival strawberries’ in mite exclusion cages. ...................................... 36

3-2 Total explained Y-variance for the different computed factors of full spectrum partial least squares regression (PLSR) analysis. .............................................. 37

3-3 Full Spectrum PLS model generated predictions of two spotted spider mite counts plotted against reference mite counts. .................................................... 37

3-4 Factor 2 scores compared to factor 1 scores for the full spectrum PLS model. .. 38

3-5 Weighted partial least squares regression coefficients showing significant wavelengths for the full spectrum PLS model. .................................................... 38

3-6 Five component full spectrum PLS model prediction for the randomly selected predictions dataset. .............................................................................. 39

3-7 Total explained Y-variance for the different computed factors of the VIS+NIR partial least squares regression (PLSR) analysis. .............................................. 39

3-8 VIS+NIR PLS model generated predictions of two spotted spider mite counts plotted against reference mite counts. ................................................................ 40

3-9 Factor 2 scores compared to factor 1 scores for VIS+NIR model. ...................... 40

3-10 Weighted partial least squares regression coefficients showing significant wavelengths for the VIS+NIR PLS model. .......................................................... 41

3-11 Four component VIS+NIR PLS model prediction for the randomly selected predictions dataset. ............................................................................................ 41

3-12 Total explained Y-variance for the different computed factors of the VIS partial least squares regression (PLSR) analysis. .............................................. 42

3-13 VIS PLS model generated predictions of two spotted spider mite counts plotted against reference mite counts. ................................................................ 42

3-14 Factor 2 scores compared to factor 1 scores for VIS model. .............................. 43

3-15 Weighted partial least squares regression coefficients showing significant wavelengths for the VIS PLS model. .................................................................. 43

3-16 Six component VIS PLS model prediction for the randomly selected predictions dataset. ............................................................................................ 44

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4-1 The Canon Powershot SX50 HS camera mounted on a tripod. ......................... 55

4-2 A 2 x 2 x 2 meter light diffuser was made out of PVC tubing and translucent white plastic sheeting. ........................................................................................ 56

4-3 Leaf samples were taken from a standard arrangement of three strawberry plants in the center of the image frame for digitization. ...................................... 57

4-4 Flat-field calibration was used to convert the raw digital numbers (DN) (0-255), into relative reflectance values. ................................................................. 58

4-5 Correlation matrix showing linear regression models for all strawberry varieties sampled in 2014 ................................................................................... 59

4-6 Correlation matrix showing linear regression models for all strawberry varieties sampled in 2015. .................................................................................. 60

5-1 The mean number of mite motiles per strawberry trifoliate throughout the 2013-2014 field season. ..................................................................................... 67

5-2 The mean number of mite eggs per strawberry trifoliate throughout the 2013-2014 field season. .............................................................................................. 68

5-3 The mean number of mite motiles per strawberry trifoliate throughout the 2014-2015 field season. ..................................................................................... 69

5-4 The mean number of mite eggs per strawberry trifoliate throughout the 2014-2015 field season. .............................................................................................. 70

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LIST OF ABBREVIATIONS

GRVI

IPM

NDVI

NIR

PLSR

Green-red vegetation index

Integrated Pest Management

Normalized Difference Vegetation Index

Near-infrared

Partial Least Squares Regression

RGB Red-Green-Blue

RMSE Root Mean Square Error

RMSEP Root Mean Square Error of Prediction

RMSPE Root Mean Square Prediction Error

RMSPEcv Root Mean Square Prediction Error of the Cross Validation

SFVIPM Small Fruit and Vegetable Integrated Pest Management

TSSM Twospotted Spider Mite

VARI Visible Atmospherically Resistant Index

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

EVALUATION OF IN SITU DETECTION METHODS FOR TWOSPOTTED SPIDER

MITES (Tetranychus urticae Koch) IN STRAWBERRY

By

Christopher D. Crockett

August 2015

Chair: Oscar E. Liburd Major: Entomology and Nematology

Previous research aimed at developing a sampling and detection program using

remote sensing with the Greenseeker® near infrared (NIR) sensor successfully

recognized infested strawberry plants with populations of twospotted spider mites

(TSSM) Tetranychus urticae Koch greater than 1000 individuals per trifoliate leaf. This

previous work shows it is possible to characterize leaf reflectance patterns on

strawberry due to arthropod damage using remote sensing, but is limited to detecting

high damage expression beyond the economic threshold of 20-30 mites per strawberry

leaf.

To address this issue, lab-based leaf spectroscopy, as well as visible and near

infrared imagery were utilized to characterize reflectance from strawberry leaves with

varying amounts of spider mite injury.

In a greenhouse based spectroscopy study performed on strawberry varieties

‘Albion’, and ‘Florida Festival’, three Partial Least Squares Regression (PLSR) models

were developed on three different spectral ranges (405-2295 nm, 405-685 nm, 405-905

nm).

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Individual single red, green, blue, and near-infrared band values and vegetation

index values calculated from these bands were found to be correlated with TSSM

counts differently for different strawberry varieties.

In a two year field study performed on strawberry varieties ‘Albion’, and ‘Florida

Festival’, it was found that there were significant differences between the number of

TSSM motiles and eggs found on each variety. This suggests that there may be

differences in varietal susceptibility, but should be further explore with controlled

experiments.

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CHAPTER 1 INTRODUCTION

During the 2013 growing season 23,549 hectares of strawberries, Fragaria

ananassa Duchesne, were harvested across ten U.S. States including California,

Florida, Michigan, New York, North Carolina, Ohio, Oregon, Pennsylvania, Washington,

and Wisconsin. Production in these states comprises a 2.5 billion USD industry (USDA-

NASS 2013) and accounts for 29 percent of the global production of strawberries

(Boriss et al. 2014). Florida is the second largest strawberry producer in the U.S.,

behind California, with Florida growers producing ~ 3,400 hectares valued at 267 million

USD during the 2013 strawberry season (USDA-NASS 2013). Florida production is

especially important during the winter months as it produces nearly all of the

domestically grown strawberries (Boriss et al. 2014, Mossler and Nesheim 2003).

However, US strawberry production faces intense competition from foreign strawberry

production. In fact, strawberry imports have steadily increased from 198 billion pounds

in 2010 to 302 billion pounds in 2012, with Mexico being the main foreign supplier

(Boriss et al. 2014). Due to the potential challenges the Florida strawberry industry may

face in light of international competition, it is important to maintain high quality and

efficient production to ensure adequate and reliable profits from the industry. In addition,

it is necessary to exert significant research and management efforts to further address

the vast array of pest problems associated with strawberry production in Florida. These

pests include insects, plant pathogens, weeds, and agriculturally impactful mite species.

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Historical control strategies for many agricultural pests have relied heavily on the

widespread use of conventional pesticides to limit pest populations. With the emergence

of pest management, after the insecticide era of the 1950’s, the use of selective

pesticides and other pest specific strategies have been ubiquitously adopted (Pedigo

and Rice 2009). These strategies incorporate many aspects of pest biology, as well as

monitoring and correct identification, to limit pest populations.

The twospotted spider mite (TSSM), Tetranychus urticae Koch, is widely

considered the most detrimental arthropod pest affecting field and greenhouse

strawberries in North Florida (Fraulo et al. 2008). In high densities TSSM can negatively

affect leaf development and subsequent fruit production (Nyoike and Liburd 2013).

Foliar damage caused by TSSM feeding is characterized by chlorophyll bleaching of the

leaf mesophyll area (Sances et al. 1979). Damage detection is difficult to recognize at

low mite densities due to the initial damage being restricted to the underside of the leaf

with the top leaf surface remaining asymptomatic. At higher densities, mite feeding can

cause white and yellow spots to appear on the upper leaf surface as a result of

increased removal of chlorophyll from the mesophyll cells, specifically the palisade

parenchyma cells (Sances et al. 1979). This continuous removal of leaf chlorophyll has

subsequent negative effects on photosynthetic activity, which leads to decreases in

plant yield, especially in younger life stages of the strawberry plant (Nyoike and Liburd

2013, Sances et al. 1979 Wyman et al. 1979). Because of this, it is important to develop

early detection methods, so that monitoring efficiency can be improved, and preemptive

treatment protocols can be implemented to prevent further establishment and spread of

TSSM populations in the field. All of these strategies are integral in developing and

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implementing a comprehensive and robust integrated pest management strategy (IPM)

for TSSM in strawberry production.

Current TSSM management practices rely heavily on the use of acaricides.

These practices are limited in their effectiveness due to the inconspicuous injury caused

by feeding of low density TSSM populations, as well as the highly clumped distribution

of TSSM in strawberry fields. Field-wide spraying practices that do not adequately takes

into account the clumped distribution of spider mites can lead to overuse of miticides

(Greco et al. 1999). These practices are unsustainable and introduce unnecessary

amounts of miticides into the environment. The development of more effective detection

and monitoring strategies is warranted so that growers can more accurately time their

management approach, as opposed to relying on routine calendar sprays.

The use of remote sensing techniques has been incorporated in a variety of

applications involving the detection of plant stress and physiological status (Peñeulas

1998). The detection of plant stress caused by the feeding of TSSM has been shown on

a number of crops including cotton, peppers, peach, and strawberry (Herrmann et al.

2012, Fraulo et al. 2009, Luedeling et al. 2009, Reisig and Godfrey 2007). Early

detection of plant stress due to mite infestation will be valuable and strategic for many

strawberry growers, because they can implement management tactics before the

problem deteriorates and become uncontrollable. The ability to implement these

detection strategies could greatly improve the management system of TSSM for field

grown strawberries. The goal of this research is to discover significant wavelengths

associated with TSSM feeding through the use of lab based leaf spectroscopy in order

to inform and develop an inexpensive and readily implementable imaging system to

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accurately detect TSSM population levels in strawberry, utilizing in situ multispectral

imaging.

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CHAPTER 2 LITERATURE REVIEW

Twospotted Spider Mite

At high infestation levels, twospotted spider mite Tetranychus urticae Koch

(Acari: Tetranychidae) (TSSM) feeding causes chlorosis visible on the adaxial side of

strawberry leaves (Liburd et al. 2007, Sances et al. 1982). Chlorosis of the adaxial leaf

surface is commonly known as stippling due to the appearance of white and yellow

spots. If mite populations are left uncontrolled, the injury to strawberry leaves can lead

to economic damage from yield reductions (Nyoike and Liburd 2013). Reduction in yield

results from decreased photosynthetic activity, due to the removal of chlorophyll from

the leaf mesophyll layer by mite feeding.

Life Cycle

The life cycle of the TSSM consists of five distinct life stages: egg, larvae,

protonymph, deutonymph, and adult. The eggs are spherical and laid on the underside

of leaves, nested in fine silk strands. The eggs take approximately three days to hatch

(Fasulo and Denmark, 2000). Total development from the egg to the adult mite takes

approximately five to twenty days under optimal temperatures ranging from 24°C to

29°C (Boudreaux, 1963).The protonymph and deutonymph life stages each have an

active (feeding) stage, followed by a quiescent (non-feeding) stage, as is typical of other

mite species in the family Tetranychidae (Herbert, 1981). The cumulative development

threshold for the immature stages is approximately 10 °C, and across temperature male

immature stages develop at a faster rate than females (Herbert, 1981). Adult females

are longer lived than adult males, ranging from two to four weeks (Herbert, 1981; Fasulo

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and Denmark, 2000). During the ovipositional phase they are capable of laying several

hundred eggs (Fasulo and Denmark, 2000).

In Field Distribution and Behavior

Though many tetranychid species, including TSSM, are generally found in low

densities scattered through natural ecosystems, in managed agroecosystems the

intrinsic rate of growth is high, allowing mite populations to reach high densities

(Kennedy and Smitley, 1985). The factors contributing to these high densities are the

suppression of natural enemy populations by agricultural pesticides, and practices

which increase the available plant host biomass.

Spider mite populations ubiquitously display different dispersal behaviors based

on a variety of stimuli. At low population densities mites have been shown to engage in

intra-plant dispersal by means of crawling (Wanibuchi and Saito, 1983). At high

population densities, and in the presence of natural enemies, spider mites have been

shown to disperse, by crawling to surrounding plants (Bernstein, 1984).

In addition to dispersal by means of crawling, TSSM has been shown to disperse

aerially. This dispersal behavior is achieved by positioning the forelegs upright, allowing

the mite to be carried aloft by wind gusts (Kennedy and Smitley, 1985). This behavior is

regularly observed in adult females and nymphal stages, though rarely observed in

adult males (Brandenburg and Kennedy, 1982a).

Current Management Strategies for the Twospotted Spider Mite

Currently, the most widely used management strategy for TSSM is the

application of chemical miticides to the entire field (Greco et al. 1991, Liburd et al.

2007). In order to determine if mite populations are significant enough to treat with

pesticides, the number of motile mites (all stages of adults) and eggs must be

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quantified. This monitoring approach can be done either in situ with the aid of a hand

lens, or ex situ by quantifying motile mites and eggs with the use of a dissecting

microscope in the laboratory. The major limitation to in situ counts is that a simple hand

lends is generally not sufficient to accurately count all of the eggs and motile mites

present, and can only serve as a rough estimate of population densities. The major

limitation to ex situ counts is the amount of time and labor required to process the leaf

samples.

The economic threshold for TSSM in strawberry is defined by the presence of

any mite life stages on trifoliate leaves and it varies according to the region of the

country and variety used. In Florida, this represents 5% of the trifoliates infested with

TSSM or roughly 20-30 mites per trifoliate in winter months (Mossler and Nesheim

2007, Rawworth 1986, Nyoike and Liburd 2013). Once the economic threshold is

reached, growers often turn to the use of a series of chemical miticides (abamectin,

bifenazate, hexythiazox, and etoxazole) for control (Mossler and Nesheim 2007, Liburd

et al. 2007). Often times these chemical control measures are applied routinely on a

calendar basis, instead of using site specific monitoring and management techniques. In

addition to chemical miticides, the use of predatory mites for control of TSSM has been

extensively examined (Fraulo et al. 2008, Rhodes et al. 2006, Greco et al. 2005).

Predatory mite species such as Phytoseiulus persimilis Athias-Henriot and Neoseiulus

californicus McGregor have been shown to significantly reduce TSSM populations when

used alone, and in conjunction with bifenazate (Rhodes et al. 2006). Although chemical

pesticides can be an effective management strategy for TSSM, caution must be used in

order to avoid possible negative effects on predatory mite programs (Cloyd et al. 2006)

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as well as the possibility of pesticide resistance in TSSM. Price et al. (2002) showed a

tenfold resistance in mites found on strawberry fields as compared with mite populations

taken from the same fields two years prior. The possibility of chemical resistance in

TSSM to chemical miticides is incredibly challenging to IPM of TSSM, and alternative

strategies must be incorporated to alleviate the reliance on field-wide spraying (Price et

al. 2002).

Leaf Spectroscopy

Spectroscopy allows light to be characterized at an incredibly fine scale at

multiple wavelength ranges across the electromagnetic spectrum. In addition to being

able to characterize the properties of light within the range of human vision, it also

allows the characterization of light properties above and below the range of human

vision. This technology has the potential to provide humans a means to characterize

and detect the symptoms of physiological phenomena in plants. In the case of arthropod

feeding damage, such as TSSM feeding, leaf spectroscopy has the potential to detect

foliar symptoms before they become apparent to the human eye. It is because of this

that I have implemented the use spectroscopy on strawberry leaves as a potential

means of TSSM detection. Through the identification of key spectral regions associated

with TSSM damage on strawberry leaves, individual narrow (and maybe broad) bands

can be utilized to develop multispectral imaging systems that are adapted specifically

for TSSM damage detection. This can be accomplished through the use of

commercially available lenses and filters that are designed to be interchangeable on

certain multispectral imaging platforms, as well as the modification of light filter

assemblies on existing off-the-shelf digital cameras.

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Digital Imagery

Digital imagery of vegetation employs the use of various imaging technologies to

observe a vast array of biological phenomena in the natural environment. The number

of different techniques that can be implemented allow indirect observation of

physiological conditions that may alter the way in which plants absorb and reflect light

back into their environment. Due to the visually apparent damage caused by TSSM at

high infestation levels, it may be possible to utilize standard broad RGB image bands to

create predictive image based analyses to estimate the levels of TSSM infestation in the

field. Information obtained through spectroscopy can determine which of these singular

broad bands, or broad band combinations, might have the most utility in detecting

TSSM leaf damage. Standard high resolution RGB images, can also be utilized in

conjunction with multispectral imaging platforms that can capture narrower wavelength

bands in the visual and near infrared regions of the electromagnetic spectrum. This

could potentially improve the detection of TSSM damage, and distinguish this type of

damage from other physiological phenomena that may be affecting leaf reflectance. The

main advantage of developing imaging technologies to detect mite damaged strawberry

plants in the field is that it will reduce time and labor for pest scouting, and could

potentially assist growers in determining when to spray miticides, by monitoring spectral

changes in strawberry plants in a site specific manner versus traditional whole-field

spraying regiments.

Study Objectives

1. To determine significant wavelengths associated with twospotted spider mite

infestation using lab-based leaf spectroscopy, and develop a model to predict mite

infestation levels.

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2. To evaluate the effectiveness of digital imagery in detecting strawberry plants

infested with varying levels of twospotted spider mites.

3. To determine differences in TSSM populations between two different varieties of

strawberry, ‘Florida Festival’ and ‘Albion’, as observed on a commercial farm.

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CHAPTER 3

DETECTION AND PREDICTION OF TWOSPOTTED SPIDER MITE (TETRANYCHUS URTICAE KOCH) INFESTATION LEVELS ON STRAWBERRY LEAVES USING

VISUAL/NIR SPECTROSCOPY

Introduction

An alternative approach to time consuming and labor intensive monitoring

techniques is the use of various remote sensing techniques to quickly and accurately

quantify TSSM populations on strawberry leaves. Accurate and precise identification of

TSSM hotspots in strawberry fields also has the potential to significantly reduce the

amount of chemical miticides used on entire fields for management of TSSM. This not

only decreases the potential for miticide resistance in TSSM, but also reduces the

impact to the surrounding environment (Price et al. 2002, Greco et al. 1999).

As light strikes a plant leaf a portion of the light is reflected back towards the

observer. Properties of reflectance are controlled by the external structure, internal

structure, and biochemical makeup of the plant (Peñuelas 1998). In leaves that are

healthy, reflectance is generally low in the visible portion of the electromagnetic

spectrum (400-700 nm) due to the absorption of blue and red light by photosynthetic

pigments (Gates et al. 1965). Internal leaf tissues reflect near-infrared (NIR) light (700-

1,900 nm) causing higher reflectance peaks when compared with other wavelengths

(Nilsson 1995). The reduction of leaf water content has also been shown to increase

reflectance in the NIR portion of the spectrum, making it a potential indicator of feeding

damage by arthropods that remove cellular material containing high amounts of water

(Carroll et al. 2008).

The characterization of spectral reflectance properties of a number of different

crops has been shown to be an effective means of detection for TSSM. Reisig and

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Godfrey (2007) showed that damage by both the cotton aphid, Aphis gossypii Glover,

and TSSM could be detected using a ground based spectrometer, though the

differences between aphid and TSSM damage could not be determined. Luedeling et al.

(2009) utilized multiple remote sensing approaches to show that peach leaves with high

levels of TSSM infestation had higher reflectance in the visible region and lower

reflectance in the nearest infrared region as compared with leaves with low infestation

levels. This study also identified wavelengths that were significantly correlated with mite

damage on peach leaves, with the highest model coefficient found in the ultraviolet

portion of the spectrum at 365 nm. Other significant wavelengths were found in the

green (497 nm), the red (687 nm), the red edge in the near infrared (744 nm), and

several other bands above 1400 nm (1405, 1888, 2500 nm). Lower resolution red,

green, and blue band multispectral aerial image analysis has also shown potential in

detecting relative mite damage levels in peach orchards (Luedeling et al. 2009). Study

by Fraulo et al. (2009), found that spider-mite damage caused characteristic increases

in reflectance at 760 nm, and between 800-1300 nm. This study also observed more

subtle changes in the green portion of the light spectrum (520-580nm). Nyoike et al.

(2012) found peaks in absorption at the edges of the 419-680 nm range, which are

associated with chlorophyll activity, and complementary to the reflectance peak

observed in the 520-580 nm range in other studies.

Early identification of TSSM feeding damage has also been shown in pepper

leaves utilizing reflectance measurements from the visible and near-infrared regions of

the electromagnetic spectrum (Herrmann et al. 2012). The objective of this particular

study was to further identify key diagnostic wavelengths associated with twospotted

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spider infestation, to develop a predictive model that could accurately estimate mite

infestation level, and to provide useful information regarding which areas of the

electromagnetic spectrum could potentially be utilized in detection methods using lower

cost broad band multispectral imagery.

Materials and Methods

Strawberry Plants

A greenhouse experiment was conducted in the fall of 2014 until spring of 2015

in the Small Fruit and Vegetable IPM (SFVIPM) greenhouse in the Department of

Entomology and Nematology, University of Florida, Gainesville, FL. Two different

strawberry varieties, ‘Florida Festival’ and ‘Albion’, were used in the experiment.

Transplants were planted in 1 liter black pots using Metromix 360 potting soil (SunGro

Horticultural Distributors Inc., Bellevue, WA), and were fertilized once every two weeks

with 15 mL of Diamond Brand fertilizer 10:10:10 (N,P,K) before watering. The plants

were watered every day for the first week, and three times a week for the remainder of

the experiment. The miticide Acramite® (Bifenazate), was sprayed prophylactically every

two weeks to prevent mite infestation on test plants, and a rotation of fungicides

(Pristine®, pyraclostrobin, BASF Corporation, Ludwigshafen, Germany) (Captevate®,

Captan + Fenhexamid, Arysta LifeScience North America LLC, Cary, NC) (Topsin®,

Thiphanate-methyl (dimethyl[1,2-phenylene)-bis(iminocarbonothioyl)]bis[carbamate]),

United Phosphorus, Inc., King of Prussia, PA) were sprayed in response to fungal

infestation, which delayed the start of the experiment by one month. One week prior to

the start of data collection, plants were placed in cylindrical mite exclusion cages made

with no-thrips 70 µm x 70 µm insect screening (Green-Tek Inc., Edgerton, WI) (Figure 3-

1). Cages were not placed on the plants until one week prior to the start of experimental

27

mite inoculation to avoid an increased risk of fungal infestation from a prolonged

reduction in airflow. All plants were sprayed with fungicide (Topsin®) and miticide

(Acramite®) one last time before caging to ensure plants were free of diseases and

mites prior to mite release.

Twospotted Spider Mite Colony

A TSSM colony was established and maintained on a random assortment of 18

Florida Festival and 16 Albion strawberry plants placed inside of a screen house for the

duration of the experiment. Only mites reared on strawberry plants were used in the

experiment to eliminate the possibility of predisposition to other host plants. The colony

was subjected to ambient weather conditions (75 - 95 oF, 14:10 L:D 80% RH), and the

strawberry plants were watered three times a week.

Experimental Design and Sampling

Strawberry plants were arranged on a greenhouse bench in a complete

randomized block design consisting of 2 variety treatments (Florida Festival and Albion),

4 TSSM inoculation treatments (0, 5, 10, and 20 mites per plant), and 8 block replicates,

yielding 64 plants in total. Initial inoculation treatments were chosen to achieve a

distribution of low, medium, and high mite infestation levels according to Nyoike (2012).

In order to inoculate strawberry plants in the mite exclusion cages, TSSM from the

established colony were transferred onto strawberry leaf discs using a small mite brush

and a dissecting microscope (Leica Z6, Leica Microsystems, Houston, TX). Mites were

transferred onto the leaf discs according to the numbers specified by the previously

mentioned inoculation treatments, and placed on the strawberry plants at the base of a

mid-tier leaf petiole using an inert water soluble glue (Elmer’s Products, Inc.,

Westerville, Ohio).

28

Two weeks after initial mite introduction one trifoliate was sampled per plant (8

per treatment), for a total of 64 trifoliate samples, and placed in an individually labeled

plastic bag (Ziploc, S.C. Johnson & Son Inc., Racine, WI) for spectral sampling, and

mite quantification. Mites were counted under a dissecting microscope (Leica Z6, Leica

Microsystems, Houston, TX).

Leaf Reflectance Spectroscopy

Prior to mite quantification, trifoliates were placed in a cooler and transported to

the Forest Ecology Laboratory at the University of Florida, Gainesville, FL, for spectral

measurement. Spectral measurements were taken within 1 hour of trifoliate sampling to

minimize the effect of dehydration on leaflet reflectance spectra. Reflectance

measurements were collected using a FieldSpec 4SR spectroradiometer (ASD Inc.,

Boulder, CO) over a 350 to 2500 nm spectral range using a sampling interval of 1.4 nm

at 350-1050 nm, and 2 nm at 1000-2500 nm. The spectral resolution was 3 nm at 700

nm, and 10 nm at 1400/2100 nm. To eliminate the effects of directional reflectance

features coming from leaflet samples, reflectance measurements were taken using an

RTS-3ZC integrating sphere (ASD Inc., Boulder, CO), with a polytetrafluoroethylene

(PTFE) Spectralon® (Labsphere, Inc., North Sutton, NH) internal coating, and a

calibrated nominal 99% Zenith® reference standard (Labsphere, Inc., North Sutton,

NH). Before any reflectance measurements were taken, the spectroradiometer was

allowed to warm up for 45 minutes to minimize the occurrence of spectral phenomena

from non-calibrated operation temperatures, and the collimated light source used in the

integrating sphere was allowed to warm up in its low power setting for 30 minutes. The

lower power setting of the light source was used for all reflectance measurements. A

reflectance reference measurement was made using the PTFE calibrated reference

29

standard in the 0° sample port position perpendicular to a collimated light source, and a

leaf sample placed in the 8° reflectance comparison port with a light trap. Subsequent

sample measurements were taken with the reference standard in the 8° comparison

port, and the leaflet samples placed in the 0° sample port with a light trap to allow for

diffuse reflectance measurements. A total of 20 reflectance measurements were taken

from the part of the adaxial leaf surface that best represented the mite infestation level.

Data Preprocessing and Analysis

Spectra Transformation

The 20 individual reflectance scans obtained for each leaflet sample were

averaged together post hoc using R version 3.2.0 statistical software (R Core Team,

2015) to increase the signal to noise ratio of the reflectance spectra, resulting in 64

reflectance spectral observations with 2151 wavelength variables. Due to signal noise

observed in the UV and SWIR ends of the reflectance spectra, the data set was

truncated to 1900 wavelength variables ranging from 401 nm to 2300 nm. Reflectance

spectra were normalized to the area under the curves, and were subsequently averaged

using 10 nm bands to reduce the data set to 190 wavelength bands from 405 nm to

2295 nm.

Partial Least Squares Regression (PLSR)

Partial least squares regression (PLSR) analysis (Martens and Naes, 1989) were

carried out using all, and subsets of, the 190 spectral bands from the reduced set as

predictor values, and mite count as the response variable. Three individual models were

developed using different wavelength ranges. The first model was developed using the

full 190 averaged bands from 405 nm to 2295 nm. The second model was developed

using 51 averaged bands from 405 nm to 905 nm, representing the visual plus the near-

30

infrared spectrum (VIS+NIR). The third model was developed using 31 averaged bands

from 405 nm to 685 nm, representing just the visual spectrum (VIS).

The PLSR analysis was carried out using the PLSR protocol in Unscrambler X

10.3 (Unscrambler Program, Camo, Norway). For each PLS model, the dataset was

randomly split into a calibration and validation set, and the maximum number of model

factors was set at 7. Student’s t tests were performed on mite counts from the

calibration and validation sets as well as mite counts from the two varieties included in

the model to make sure all data used in the calibration and validation sets were

representative of the total dataset. All variables were divided by their standard deviation

to properly weight all variables, and eliminate the influence of variable scale on the

model. The models were carried out the using a wide-kernel PLS protocol and a full

cross-validation was run on the data set. Important wavelength variables were

visualized, and the predictive power of each model was evaluated using the randomly

selected validation set.

Results

Twospotted Spider Mite Populations

The mean number of twospotted spider mites was not significantly different

between variety [Albion and Florida Festival] (t = 0.590, p = 0.557), nor was it

significantly different between the calibration and validation data sets (t = -0.094, p =

0.926). The mean (± S.E.) number of twospotted spider mites was 70.4 ± 32.6, and 44.4

± 20 mites/trifoliate for the ‘Albion’ and ‘Florida Festival’ varieties respectively. The

mean number of twospotted spider mites was 55.3 ± 23.4 mites/trifoliate, and 59.6 ±

37.6 mites/trifoliate for the calibration dataset and validation set respectively.

31

PLS Model Development and Prediction

Figure 3-2 shows the optimum number of factors that explain the most variance

in the calibration data set and its cross validation for the full spectrum model. The full

model was allowed to compute a maximum of 7 factors to explain the variance in mite

number, though the model showed that the explained variance in mite number was

optimized for both calibration and cross-validation at 5 factors. The developed PLS

model explained 89.3% of the mite variance with a root mean squared error (RMSE) of

42.6 mites/trifoliate, and the cross-validation of the model explained 74.6% of mite

variance with an RMSE of 68.0 mites/trifoliate (Figure 3-3). There was no observable

clustering of the strawberry varieties across factor 1 which explained 69% of the

variance in wavelength variables, and 25.0% of the variance in mite number, but there

was clustering across factor 2 which explained 15.0% of variance in wavelength

variables, and 27.0% of the variance in mite counts (Figure 3-4). Figure 3-5 shows the

wavelengths that are significantly correlated with mite number when 5 factors were

included in the PLS model. The full spectrum PLS model prediction explained 19.2% of

the predicted mite variance with a root mean squared error of prediction (RMSEP) of

188.0 mites/trifoliate (Figure 3-6).


in the calibration data set and its cross validation for the VIS+NIR model. The model

was allowed to compute a maximum of 7 factors to explain the variance in mite number,

though the model showed that the explained variance in mite number was optimized for

both calibration and cross-validation at 4 factors. The developed VIS+NIR PLS model

explained 90.1% of the mite variance with a root mean squared error (RMSE) of 41.2

mites/trifoliate, and the cross-validation of the model explained 68.8% of mite variance

32

with an RMSE of 75.3 mites/trifoliate (Figure 3-8). There was no observable clustering

of the strawberry varieties across factor 1 which explained 48.0% of the variance in

wavelength variables, and 48.0% of the variance in mite number, and there was no

observable clustering across factor 2 which explained 15.0% of variance in wavelength

variables, and 15.0% of the variance in mite counts (Figure 3-9). Figure 3-10 shows the

wavelengths that are significantly correlated with mite number when 4 factors were

included in the PLS model. The PLS model prediction explained 15.5% of the predicted

mite variance with a root mean squared error of prediction (RMSEP) of 192.3

mites/trifoliate (Figure 3-11).


in the calibration data set and its cross validation for the VIS model. The model was

allowed to compute a maximum of 7 factors to explain the variance in mite number,

though the model showed that the explained variance in mite number was optimized for

both calibration and cross-validation at 6 factors. The developed VIS PLS model

explained 76.7% of the mite variance with a root mean squared error (RMSE) of 63.1

mites/trifoliate, and the cross-validation of the model explained 13.3% of mite variance

with an RMSE of 125.5 mites/trifoliate (Figure 3-13). There was no observable

clustering of the strawberry varieties across factor 1 which explained 70.0% of the

variance in wavelength variables, and 24.0% of the variance in mite number, but there

was some observable clustering across factor 2 which explained 16.0% of variance in

wavelength variables, and 17.0% of the variance in mite counts (Figure 3-14). Figure 3-

15 shows the wavelengths that are significantly correlated with mite number when 4

factors were included in the PLS model. The PLS model prediction failed to explain any

33

of the predicted mite variance, and the prediction had a root mean squared error of

prediction (RMSEP) of 222.7 mites/trifoliate (Figure 3-16).

Discussion and Conclusions

While the 5 factor full spectrum PLS model described the data well (Y = 0.89X +

5.89, R2 = 0.90), in the calibration stage, the model was poor at predicting TSSM

numbers on strawberry leaves using leaf spectra alone (RMSEP = 188.72

mites/trifoliate). This amount of prediction error would most likely be deemed

impractical, in a field setting as this is much higher than the economic threshold of 20-

30 mites/trifoliate leaf. In the case where PLS uses a high number of components to

predict a single response variable it is not entirely certain that the model is describing

truly latent wavelength variable effects versus merely describing superficially apparent

wavelengths. The ranges from 505-515 nm, 585-685 nm, 725-855 nm, 1155-1325 nm,

and 1405-1445, each comprised of 10 nm averaged bands, were those most significant

wavelengths found in this model, and share both similarities and differences with

wavelength characteristics previous studies. Fraulo et al. (2009) described

characteristic increases at 760 nm, and 800-1300 nm, and lesser changes in the green

region between 520-580 nm. This particular study identified important wavelengths

similarly near the ‘red-edge’ character of leaf reflectance (760 nm), as well as

wavelengths in the NIR plateau, yet it described different important wavelengths in the

green portion of the visible spectrum. In addition, Nyoike (2012) described importance

absorbance characters in the red and blue part of the visible spectrum surrounding the

green peaks, which agrees with the important reflectance peaks around 580 nm seen in

this experiment.

34

The 4 factor VIS+NIR PLS model described the data in a similar manner to the 5

factor full spectrum PLS model (Y = 0.90X + 5.47, R2 = 0.90) in the calibration stage,

and utilized 1 less PLS factor, making it a more parsimonious model. The prediction

ability, however, was worse than the 5 factor model (RMSEP = 192.30 mites/trifoliate),

and is also far above the economic threshold for TSSM. The 4 factor VIS+NIR model

identified fewer, and different significant wavelengths associated with the number of

TSSM, compared to the 5 factor full spectrum model. The VIS+NIR model identified 415

nm, 685 nm, and the ranges from 745-805 nm and 835-905 nm. Luedeling et al. (2009),

observed similar significant wavelengths at 687 nm, and 744 nm associated with TSSM

damage in peach orchards, indicating that wavelengths in this part of the red spectrum,

and near the top of the ‘red-edge’ may be useful in detection of TSSM damage.

The 6 factor VIS PLS model did not described the data as well as the full

spectrum model, nor the VIS+NIR model (Y = 0.77X + 12.92, R2 = 0.77), in the

calibration stage, and generated the worst prediction of TSSM numbers (RMSEP =

222.74 mites/trifoliate). The VIS only PLS model also identified 415 nm as a significant

wavelength associated with TSSM infestation. This was the only significant wavelength

that the model identified. It is interesting to note that this particular wavelength has not

been previously associated with any physiological phenomena in vegetation, however,

significance at this wavelength could potentially be a product of high noise to signal ratio

at the low end of the measured spectrum.

Further study should utilized a higher number of leaf samples in order to

establish a more representative set of calibration and prediction data partitions in hopes

of improving the predictive ability of PLSR models. Due to this small sample size both

35

varieties were modeled together, which may have confounded the detection of more

significant wavelengths for TSSM detection. A large proportion of the data set contained

leaves with no TSSM present, and thus the data was distributed rather poorly across

mite infestation levels with only a couple observations of high mite infestation. These

high levels of infestation act as outliers in the model development, dramatically affecting

model development. High observations were not removed from analysis do to their

biological relevance to the model formation, and future work should focus on obtaining

more leaf samples with medium, and high infestations of TSSM. Even though the

models lacked predictive power, the agreement between wavelengths, particularly 685

nm, and 745 nm with previous study on TSSM infected plants is of interest, and should

be investigated further to determine if these wavelengths are truly associated with

TSSM levels. These particular wavelengths could be utilized to develop lenses or filters

for multispectral cameras that could be utilized in the field to improve image based

detection of TSSM damage on strawberry leaves.

36

Figure 3-1. Greenhouse setup of complete randomized block design of ‘Albion’ and

‘Florida Festival’ strawberries in mite exclusion cages. Photo courtesy of C.D. Crockett.

37

Figure 3-2. Total explained Y-variance for the different computed factors of full

spectrum partial least squares regression (PLSR) analysis for the validation set (blue), and its cross calibration (red).

Figure 3-3. Full Spectrum PLS model generated predictions of two spotted spider mite

counts plotted against reference mite counts for the calibration set of the model (blue), and the full cross-validation set (red).

38

Figure 3-4. Factor 2 scores compared to factor 1 scores, showing the clustering of

scores across factor 2 for individual strawberry variety, for the full spectrum PLS model.

Figure 3-5. Weighted partial least squares regression coefficients showing significant

wavelengths for the full spectrum PLS model. Weighted regression coefficients are considered significant if the standard error of the wavelength regression coefficient does not cross the zero axis.

39

Figure 3-6. Five component full spectrum PLS model prediction for the randomly

selected predictions dataset.

Figure 3-7. Total explained Y-variance for the different computed factors of the

VIS+NIR partial least squares regression (PLSR) analysis for the validation set (blue), and its cross calibration (red).

40

Figure 3-8. VIS+NIR PLS model generated predictions of two spotted spider mite

counts plotted against reference mite counts for the calibration set of the model (blue), and the full cross-validation set (red).

Figure 3-9. Factor 2 scores compared to factor 1 scores for VIS+NIR model, showing

the clustering of scores across factor 2 for individual strawberry variety.

41


wavelengths for the VIS+NIR PLS model. Weighted regression coefficients are considered significant if the standard error of the wavelength regression coefficient does not cross the zero axis.

Figure 3-11. Four component VIS+NIR PLS model prediction for the randomly selected

predictions dataset.

42

Figure 3-12. Total explained Y-variance for the different computed factors of the VIS

partial least squares regression (PLSR) analysis for the validation set (blue), and its cross calibration (red).

Figure 3-13. VIS PLS model generated predictions of two spotted spider mite counts

plotted against reference mite counts for the calibration set of the model (blue), and the full cross-validation set (red).

43

Figure 3-14. Factor 2 scores compared to factor 1 scores for VIS model, showing the

clustering of scores across factor 2 for individual strawberry variety.


wavelengths for the VIS PLS model. Weighted regression coefficients are considered significant if the standard error of the wavelength regression coefficient does not cross the zero axis.

44

Figure 3-16. Six component VIS PLS model prediction for the randomly selected

predictions dataset.

45

CHAPTER 4 IN SITU DETECTION OF TWOSPOTTED SPIDER MITE (Tetranych urticae KOCH)

INFESTED STRAWBERRY PLANTS USING SIMPLE DIGITAL IMAGERY

Introduction

Digital imagery has been utilized for a number of different applications in

agriculture, because of its potential to provide quick and powerful monitoring tools for

phenomena ranging from canopy coverage of soybeans (Purcell 2000) to nutritional

content of wheat (Jensen et al. 2007). If high quality images are obtained from the

environment, the amount of structural and physiological information that can be

conveyed through indirect measurement is vast.

The use of vegetation indices derived from different image bands has been a

long term strategy to condense the amount of information contained in multiple bands

into a single metric that is indicative of the relationship between different color bands at

various points in the image. These indices were designed with the intention of capturing

the light signals from plants and assessing various biometric parameters such as

biomass, water use, plant stress, plant health, and crop production (Jackson and Huete

1991).

In order to assess the potential of utilizing digital imagery to extract biometric

information from strawberry plants, specifically the damage of TSSM, individual red,

green, blue, and near-infrared (NIR) bands , as well as three different vegetation indices

based off of these 4 bands were utilized. The green-red vegetation index (GRVI) was

first described by Tucker (1979), and can be utilized to threshold between vegetation

and soil/background reflectance, through the difference between green and red light.

This vegetation index assumes that vegetation will be positive (green reflectance higher

than red reflectance), soil and background will be negative (red reflectance higher than

46

green reflectance), and that water is 0 (green and red reflectance equal (Matohka et al.

2010). This particular vegetation index is described by the following equation:

𝐺𝑅𝑉𝐼 =(𝐺𝑟𝑒𝑒𝑛 − 𝑅𝑒𝑑)

(𝐺𝑟𝑒𝑒𝑛 + 𝑅𝑒𝑑)

In addition to the GRVI, the visible atmospherically resistant index (VARI) was

utilized as it is similar to the GRVI, but incorporates the blue band as well (Gitelson et

al. 2002). This vegetation index is described by the following equation:

𝑉𝐴𝑅𝐼 =(𝐺𝑟𝑒𝑒𝑛 − 𝑅𝑒𝑑)

(𝐺𝑟𝑒𝑒𝑛 + 𝑅𝑒𝑑 − 𝐵𝑙𝑢𝑒)

The normalized difference vegetation index (NDVI) was also utilized, because of

its widespread use as a measure of plant health in green vegetation, and because it

utilizes both the highest absorption region (red), and the highest reflection region (NIR)

of chlorophyll in plant leaves (Rouse et al. 1973). NDVI is described by the following

equation:

𝑁𝐷𝑉𝐼 =(𝑁𝐼𝑅 − 𝑅𝑒𝑑)

(𝑁𝐼𝑅 + 𝑅𝑒𝑑)

Unlike dense forest canopies where NDVI can saturate, strawberry canopies are

more sparse, and low to the ground, making NDVI a suitable candidate or use in

attempting to detect TSSM damage on strawberry leaves.

In healthy photosynthetically active plants GRVI, VARI, and NDVI values are

expected to be higher than in plants with some form of impairment that effects the

amount of chlorophyll present, and thus the proportion of blue, green, red and infrared

light absorbed by leaves. Strawberry plants with high amounts of feeding by TSSM are

expected to exhibit water loss and chlorosis of the leaves, and from that, a reduction in

47

the proportion of green and NIR reflected, as well as in increase in the amount of red

and blue light reflected.


Imaging Equipment

Standard RGB images were collected using a consumer grade Canon Powershot

SX50 HS camera (Canon Inc., Tokyo, Japan). This particular camera is fitted with

CMOS sensor with 12.1 effective megapixels. Near-infrared images were collected

using a modified Canon Powershot SX260 HS (Canon Inc., Tokyo, Japan), using the

same 12.1 effective megapixel CMOS sensor as the Canon Powershot SX50 HS. The

cameras were mounted onto a Manfrotto 190xPROB tripod (Lino Manfrotto + Co. Spa.,

Cassola, Italy) as shown in Figure 4-1, and the cameras were leveled on two axes using

a leveling bubble, to achieve nadir orientation with the strawberry canopy surface.

Image capture was triggered remotely with an automatic shutter switch. Two field data

collection sessions were conducted in the spring of 2014 and the fall of 2015. In the

2015 field study, a 2 m x 2 m x 2 m light tent was constructed out of PVC tubing, and

translucent plastic sheeting to act as a light diffuser for the images (Figure 4-2).

20I4 Field Season Image Collection

For the 2014 study, strawberry plant images were taken in March on a 2.02

hectare field of Albion strawberries and a 4.45 hectare of Festival strawberries, located

in Citrus County, FL. Six imaging sites were chosen in each field representing different

levels of spider mite infestations. A white and black calibration board was placed in

each frame for subsequent image processing, and the image frame was centered on a

standard arrangement of three strawberry plants in each image (Figure 4-3). Three

48

trifoliate leaves were sampled from each plant and placed in individually labeled plastic

bags (Ziploc, S.C. Johnson & Son Inc., Racine, WI). Leaf samples were placed in a

cooler and transported back to the Small Fruit and Vegetable IPM (SFVIPM) laboratory

at the University of Florida, Gainesville, FL, where mites were counted under a

dissecting microscope (Leica Z6, Leica Microsystems, Houston, TX). Mite counts for the

three trifoliates for each plant were averaged to create a mean mite counts per plant.

2015 Field Season Image Collection

For the 2015 study, a new location had to be utilized due to the lack of

twospotted spider mites at the 2014 study site. In this new location, images of

strawberry plants infested with different levels of mites were captured at the University

of Florida’s Plant Science Research and Education Unit (PSREU), Citra, FL. Two

imaging sites were chosen for four varieties of strawberry plants present in each plot

(‘Florida Festival’, ‘Radiance’, ‘Sensation’, and ‘Winterstar’) for a total of eight imaging

sites, and colored dots were used to mark individual strawberry trifoliates that were

digitized and analyzed during the image analysis phase of the study. The number of

mites were counted using the same method as the 2014 field-season, but mite counts

were not averaged together, instead giving a single mite count per trifoliate sampled on

each plant.

Image Processing and Data Analysis

The images obtained from both studies were calibrated utilizing the flat-field

calibration method in ENVI version 5.1 (Exelis Visual Information Solutions, Boulder,

Colorado). This method calibrates all pixels in the image to a standard flat-field of

reflectance (in this case a gray calibration board placed in the image) converting

computer RGB and NIR values to percentage reflectance for red, green, blue, and NIR

49

channels (Figure 4-4). Once calibrated, regions of interest were defined for each of the

sampled plants inside of the image. Ten thousand pixels were randomly chosen from

each of the regions of interest, using ENVI version 5.1, and the mean relative

reflectance was determined. For the 2014 image study, 36 whole strawberry plants

were hand digitized using ENVI, and analyzed. For the 2015 image study, 72 individual

trifoliates were hand digitized instead of whole plants. This was done to analyze only the

pixels from the trifoliate leaves that were sampled, and mites counted. Simple linear

regression was used for samples from each variety to determine if there is a significant

relationship between the single band relative reflectance as well as the GRVI, VARI,

and NDVI indices and mite infestation levels. The Breusch-Pagan test was used to test

whether or not each linear model met homoscedasticity assumptions, and the Shapiro-

Wilk test was used to check whether or not the linear models met the normality

assumptions of linear regression (Breusch and Pagan 1979, Shapiro and Wilk 1965).

Any models where residuals were not normally distributed had model variables

transformed appropriately. A Pearson’s correlation coefficient was calculated for each

set of model constituents. The mean number of spider mites per plant for ‘Florida

Festival’ strawberries in the 2014 study was square root transformed in order to meet

normality assumptions. The mean number of mites per plant for ‘Albion’ strawberries in

the 2014 study was log transformed in order to meet normality assumptions. The

number of spider mites per trifoliate for ‘Florida Festival’ strawberries in the 2015 study

was log transformed for the green single band reflectance model, the GRVI model, and

the VARI model in order to meet normality assumptions. The number of spider mites per

trifoliate for ‘Winterstar’ strawberries in the 2015 study were log transformed for the NIR

50

single band reflectance model in order to meet normality assumptions. The number of

mites per trifoliate for ‘Radiance’ strawberries in the 2015 study were log transformed

for the red, green, and blue single band reflectance models, as well as the GRVI and

VARI reflectance models, in order to meet normality assumptions. The number of mites

per trifoliate for ‘sensation’ strawberries in the 2015 study were log transformed for the

green single band reflectance model, and the NDVI reflectance model, in order to meet

normality assumptions. Leave-one-out cross validation was carried out on all of the

models to generate a root mean square prediction error for each model

Results

20I4 Field Season Image Study

Figure 4-5 shows all of the single band reflectance and vegetation index linear

models for each variety of strawberry studied in the 2014 field season. Five of the

fourteen regression models constructed for the 2014 field season were statistically

significant (Table 4-1).

20I5 Field Season Image Study

Figure 4-6 shows all of the single band reflectance and vegetation index linear

models for each variety of strawberry studied in the 2015 field season. Fourteen of the

twenty-eight regression models constructed for the 2015 field season were statistically

significant (Table 4-2).


Out of the single reflectance bands, the green band, and the NIR band were

found to have significant negative correlations to TSSM numbers on ‘Florida Festival’

strawberries, in 2014. The direction of these relationships is as expected due to the

decrease in green and NIR reflectance one would expect to see in plants infested with

51

TSSM as they feed and evacuate both chlorophyll and constituent water from the

palisade parenchyma cells of the strawberry leaves. It is important to note, however,

that this observed negative relationship between green band reflectance and TSSM

numbers was not observed for ‘Florida Festival’ strawberries in 2015, but the significant

negative relationship between NIR band reflectance and TSSM numbers was similar

between the two years of study. This might be due to a difference in the plant condition

and growing locations between years. None of the derived vegetation indices were

significant predictors for ‘Florida Festival’ strawberries, in 2014, which could be caused

by an unexpected negative relationship between red band reflectance and TSSM

numbers, though NDVI was a significant predictor in 2015 showed a moderate negative

relationship with TSSM numbers. This unexpected relationship observed could have

been a result direct illumination of the strawberry plants in the 2014 study versus the

diffuse illumination of the strawberry plants in the 2015 study.

Green band reflectance had a significant negative correlation with TSSM

numbers on ‘Albion’ strawberry plants similar to ‘Florida Festival’. The GRVI and VARI

also had strong negative correlations to TSSM numbers on ‘Albion’ strawberries. This is

expected due to the similarity between GRVI and VARI, and the relationship between

the decrease in green reflectance and the increase in red reflectance as TSSM levels

increase. A few of the GRVI values for ‘Albion’ strawberries were negative, which is

uncharacteristic of vegetation, and could be indicative of dead vegetation almost entirely

absent of chlorophyll.

Red band reflectance was significantly correlated with TSSM infestations for

‘Winterstar’ and ‘Radiance’ strawberries, and exhibited a positive relationship with

52

TSSM numbers. Blue band reflectance was also significantly correlated with ‘Winterstar’

and ‘Radiance’ strawberries, showing a positive relationship. Chlrophyll a has

absorption peaks in both the red and blue regions of the visible spectrum, and this

increase in blue reflectance could be the result of chlorophyll being drawn out of the leaf

cells, and no longer being present in the cell to absorb as much blue light. Both

‘Winterstar’ and ‘Radiance’ exhibited significant and strong negative correlations

between NIR reflectance and TSSM numbers, which again could be caused by the

decrease in leaf water content from high levels of TSSM feeding. All three vegetation

indices were negatively correlated with TSSM numbers for ‘Winterstar’ and ‘Radiance’,

and had some of the lowest root mean square prediction errors out of all of the

developed models.

None of the single band reflectance values, nor any of the vegetation indices

were significantly correlated with TSSM numbers found on ‘Sensation’ strawberry

plants. Though the models were none significant the red, green, and blue reflectance

values, and GRVI and VARI values, behaved the exact opposite of what was expected

with red and blue being negatively correlated with TSSM numbers, and green

reflectance, GRVI, and VARI being slightly positively correlated. Future study should

focus on identifying varieties, such as ‘sensation’, that might respond in an unexpected

way to TSSM feeding, and attempt to discern the mechanisms as to why these varieties

may behave entirely opposite of other strawberry varieties.

Though some of the models were highly significant in this study, and displayed

strong correlations with TSSM numbers, these results primarily serve as a means to

describe the relationship between changes in leaf reflectance and TSSM infestation

53

levels, due the high root mean square prediction errors observed almost ubiquitously in

all of the models. Though the most significant and strongest correlations observed had

some of the lowest RMSPE values, it should be noted that almost all of these absolute

error values are far larger than the economic threshold of 20-30 mites/trifoliate, much

like the RMSPE observed in the PLSR models. While these models are informative,

their predictive power is modest, and further sampling and analyses should be carried

out in order to more predictive models that can potentially be applied in field scouting

efforts.

These results suggest that there are considerable differences between

strawberry varieties and how their foliage responds to TSSM feeding. These observed

differences could potentially be attributed to some underlying differences in how certain

varieties respond to spider mite feeding. One possibility that might prevent adequate

image based detection utilizing certain vegetation indices in strawberries is the amount

of tolerance and symptomless expression certain strawberry varieties may exhibit in

response to TSSM feeding. Oftentimes varieties of plants are chosen to accommodate

high populations of arthropods without showing significant injury, while still achieving

satisfactory yields (Pedigo and Rice 2009). This genetic mode of resistance, also known

as tolerance, may actually reduce the ability to adequately detect the presence of TSSM

on a particular variety of strawberries. If plants that are highly tolerant to TSSM are

chosen because they produce similar yields to unaffected plants, this means that there

may not be chlorosis of the plant leaves, and thus no symptomatic expression to detect.

Tolerant strawberry varieties may have thicker leaves or higher concentrations of

palisade parenchyma cells, compared to varieties that are less tolerant, and readily

54

display TSSM Feeding symptoms. This brings up the question of how to balance the

use of this technique with traditional varietal selection that may obfuscate attempts to

detect arthropod pests using digital imagery.

Digital image analysis such as the technique described in this study may be able

to be utilized on varieties of strawberry plants that are chosen not for tolerance, but for

some other seasonal benefit, such as the ‘Albion’ variety. One of the main reasons

‘Albion’ strawberries are considered beneficial to growers is because of their early

season production, with the potential to produce sizeable yields in North-central Florida,

at a market premium during the Thanksgiving season. This early season variety of

strawberry may benefit from the use of the described digital imaging techniques

because it has a strong relationship with the GRVI and VARI values. These vegetation

indices could be employed in early season varieties such as ‘Albion’ that share this

strong correlation, and utilized for early season monitoring of TSSM in order to

adequately protect high premium production. In addition to early blooming varieties such

as ‘Albion’, growers could elect to use ‘Winterstar’, ‘Radiance’, and other similar

varieties, primarily for their predictable responses to TSSM feeding that could aid in the

use of easy detection of mite hotspots through digital imagery techniques.

55

Figure 4-1. The Canon Powershot SX50 HS camera is a simple red/green/blue (RGB)

channel digital with a 12.1 megapixel CMOS sensor, and was mounted on a Manfrotto 190xPROB tripod in order to take images of strawberry plants in the field during both years of study. Photo courtesy of C.D. Crockett

56

Figure 4-2. A 2 x 2 x 2 meter light diffuser was made out of PVC tubing and translucent

white plastic sheeting in order to provide diffuse lighting for strawberry plant images. Photo courtesy of C.D. Crockett

57

Figure 4-3. Leaf samples were taken from a standard arrangement of three strawberry

plants in the center of the image frame. The three strawberry plants were digitized with elliptical regions of interest for whole plants (2013-2014 study), and for individually sampled leaves (2014-2015 study). A grey calibration board (right) was used to perform flat-field calibration on each analyzed image. Photo courtesy of C.D. Crockett

58

Figure 4-4. Flat-field calibration was used to convert the raw digital numbers (DN) (0-

255), into relative reflectance values. Photo courtesy of C.D. Crockett

59

Figure 4-5. Correlation matrix showing linear regression models for A) Florida Festival,

and B) Albion strawberry variety mite counts, and their respective image derived single band reflectance values (green, red, blue, and NIR bands), and vegetation indices (GRVI, VARI, and NDVI). Mite counts were log transformed, square root transformed, or cube root transformed for the linear regression models where residuals were not normally distributed depending on which transformation was appropriate. Statistically significant models are marked with red asterisks.

60

Figure 4-6. Correlation matrix showing linear regression models for A) Florida Festival,

B) Winterstar, C) Radiance, and D) Sensation strawberry variety mite counts, and their respective image derived single band reflectance values (green, red, blue, and NIR bands), and vegetation indices (GRVI, VARI, and NDVI). Mite counts were log transformed for the linear regression models where residuals were not normally distributed. Statistically significant models are marked with red asterisks.

61

Table 4-1. Regression model parameters for single band and vegetation index models

for the 2014 field study. Root mean square prediction error (RMSPECV) was generated using leave-one-out cross validation. Significance level was set at p = 0.05.

Year Variety Single Band Vegetation Index r F (1, 16) R2 p RMSPECV

2014 ‘Florida Festival' red -0.428 3.598 0.184 0.076 476.666

green -0.615 9.751 0.379 *0.007 405.358

blue 0.028 0.177 0.011 0.680 14.394††

NIR -0.510 5.612 0.260 *0.031 2.874†

GRVI -0.297 1.543 0.088 0.232 2.947†

VARI -0.221 0.822 0.049 0.378 3.019†

NDVI 0.145 0.345 0.021 0.565 13.926††

‘Albion' red 0.359 2.373 0.129 0.143 3.546†

green -0.503 5.414 0.253 *0.033 583.004

blue -0.228 0.876 0.052 0.363 3.704†

NIR -0.217 0.791 0.047 0.387 3.694†

GRVI -0.847 40.720 0.718 *<0.001 2.064†

VARI -0.847 40.670 0.718 *<0.001 2.587†††

NDVI -0.331 1.973 0.110 0.179 3.581†

*p<0.05 † RMSPE in units of log(mite number) †† RMSPE in units of sqrt(mite number) ††† RMSPE in units of (mite number)^(1/3)

62

Table 4-2. Regression model parameters for single band and vegetation index models

for the 2015 field study. Root mean square prediction error (RMSPECV) using leave-one-out cross validation. Significance level was set at p = 0.05.

Year Variety Single Band Vegetation Index r F (1, 16) R2 p RMSPECV

2015 ‘Florida Festival’ red 0.266 1.214 0.071 0.287 662.997

green 0.273 1.285 0.074 0.274 1.226†

blue 0.351 2.244 0.123 0.154 593.446

NIR -0.540 6.598 0.292 *0.021 556.726

GRVI -0.204 0.692 0.041 0.418 1.267†

VARI -0.198 0.655 0.039 0.430 1.283†

NDVI -0.552 7.011 0.305 *0.018 553.206

‘Winterstar’ red 0.533 6.352 0.284 *0.023 285.156

green 0.432 3.676 0.187 0.073 307.883

blue 0.481 4.818 0.231 *0.043 296.592

NIR -0.696 15.030 0.484 *0.001 556.726

GRVI -0.593 8.694 0.352 *0.009 270.526

VARI -0.615 9.722 0.378 *0.007 261.880

NDVI -0.668 12.880 0.446 *0.002 251.549

‘Radiance’ red 0.579 8.078 0.336 *0.018 0.876†

green 0.281 1.376 0.079 0.258 1.006†

blue 0.479 4.760 0.229 *0.044 0.911†

NIR -0.836 37.280 0.699 *<0.001 277.150

GRVI -0.780 24.940 0.609 *<0.001 0.677†

VARI -0.778 24.540 0.605 *<0.001 0.688†

NDVI -0.863 46.670 0.745 *<0.001 255.010

‘Sensation’ red -0.059 0.057 0.003 0.815 281.146

green 0.136 0.300 0.018 0.591 1.033†

blue -0.104 0.174 0.011 0.682 277.678

NIR -0.220 0.817 0.049 0.379 275.455

GRVI 0.173 0.496 0.030 0.491 331.771

VARI 0.210 0.740 0.044 0.403 321.578

NDVI -0.169 0.473 0.029 0.501 1.021†

*p<0.05 † RMSPE in units of log(mite number)

63

CHAPTER 5

DIFFERENCES IN TWOSPOTTED SPIDER MITE POPULATIONS BETWEEN TWO DIFFERENT VARIETIES OF STRAWBERRY, ‘FLORIDA FESTIVAL’ AND ‘ALBION’ AS

OBSERVED ON A COMERCIAL FARM

Introduction

One of the most important aspects of growing crop plants, is the selection of

varieties with various beneficial traits that can be utilized for growers for different

purposes. Variety selection can be utilized in different regions to accommodate different

soil types, as well as different climate conditions. Crops varieties have also been

developed and chosen for increased tolerance to arthropod pests, in order to preserve

yield in the face of pest pressure (Pedigo and Rice 2009). The ‘Florida Festival’ is a

particular prominent variety of strawberry grown in Florida, due to its high tolerance of

pest pressures and its ability to produce substantial fruit yields. Another variety of

strawberry observed in this field study is, ‘Albion’. This is a rapid growing early season

variety that produces fruit during early season and thus is a beneficial variety for

growers looking to capitalize on profits during early season when market prices are

high. This field study was aimed to examine the differences in the population of TSSM

motiles and eggs, for two varieties of strawberries, ‘Florida Festival’ and ‘Albion’ grown

in Florida.


2013-2014 Field Sampling

Sampling for the 2013-2014 field study was carried out on a 40 hectare

commercial strawberry farm located in Citrus County, FL. Two varieties were used: 4-

hectares of ‘Albion’ and 9 hectares of ‘Florida Festival’. Sixty trifoliate strawberry leaves

were sampled randomly from each field biweekly from 11/18/2013 to 3/27/2014 at the

64

first siting of TSSM on the farm. Sampled trifoliates were placed into individually labeled

plastic bags (Ziploc, S.C. Johnson & Son Inc., Racine, WI), and transported back to the

Small Fruit and Vegetable IPM (SFVIPM) laboratory at the University of Florida,

Gainesville, FL. Twospotted spider mites (TSSM) motiles and eggs were counted and

recorded under a dissecting scope microscope (Leica Z6, Leica Microsystems,

Houston, TX).

2014-2015 Field Sampling

Sampling for the 2014-2015 field-season was carried out in the same fields as

the 2013-2014 study. One hundred and twenty trifoliate leaves were sampled randomly

from each field biweekly from 12/2/2014 to 3/26/2014 at the first siting of TSSM on the

farm. Sampled trifoliates were placed into individually labeled plastic bags (Ziploc, S.C.

Johnson & Son Inc., Racine, WI), and transported back to the SFVIPM laboratory at the

University of Florida, Gainesville, FL. Twospotted spider mites motiles and eggs were

counted under a dissecting scope microscope (Leica Z6, Leica Microsystems, Houston,

TX).

Data Analysis

The Kruskal-Wallis non parametric test was utilized to determine if there were

differences in the mite populations between ‘Albion’ and ‘Florida Festival’ varieties due

to the non-normal distribution of mite motiles and eggs in each of the fields sampled.

Results

2013-2014 Study

The numerical distributions of mite motiles was significantly different between

‘Albion’ and ‘Florida Festival’ varieties in the 2013-2014 field season (X2 = 8.7, df=1, p =

0.003). The numerical distribution of mite eggs was also found to be significantly

65

different between ‘Albion’ and ‘Florida Festival’ varieties in the 2013-2014 field season

((X2 = 8.2, df =1, p = 0.004).

2014-2015 Study

Similar to the 2013-2014 field study, the numerical distributions of mite motiles

were significantly different between ‘Albion’ and ‘Florida Festival’ varieties in the 2014-

2015 field season (X2 = 3.9, df=1, p = 0.049). The numerical distribution of mite eggs,

however, did not differ significantly between ‘Albion’ and ‘Florida Festival’ varieties (X2 =

3.3, df = 1, p = 0.070).


In this study the levels of motile mites and eggs were much higher in the ‘Albion’

variety for the first year of study, where a large mid-late season peak was observed

compared with the relatively low levels observed in the ‘Florida Festival’ variety (Figure

5-1). In the late season the number of TSSM motiles and eggs (Figure 5-2) for both

varieties peaked once more, following the cessation of chemical control measures. In

the second year of study, there were very few TSSM motiles (Figure 5-3) and eggs

(Figure 5-4) present for both varieties throughout the entire season. Following the

cessation of chemical control measures there was a significant increase in the number

of TSSM motiles and eggs, with a high number of motiles and eggs present in ‘Florida

Festival’ strawberry plants. Because of the difference in observations between years, it

is difficult to determine whether or not there is an increased level of resistance in one

variety versus the other variety, though in the first year of study, it appears as though

‘Florida Festival’ plants were less conducive to high mite populations. This observation,

however, may simply be due to differences in the initial levels of mites in the field from

nursery transplants, as well as differences in the effectiveness of chemical control

66

measures between the fields. Anecdotally, it was observed that there were more visible

mite hotspots that occurred in the ‘Albion’, but this has not been examined through

experimental means. Based on this preliminary observation study, controlled

experiments should be performed to determine if there is varietal difference in the

susceptibility of ‘Florida Festival’ and ‘Albion’ strawberries to TSSM. This research

would benefit growers in making decisions on which variety to implement according to

their individual growing needs.

67

Figure 5-1. The mean number of mite motiles per strawberry trifoliate throughout the

2013-2014 field season for ‘Florida Festival’ (Blue), and ‘Albion’ (Red) strawberries.

0

20

40

60

80

100

120

140

160

180

200M

ean

# o

f m

ites

/tri

folia

te

Florida Festival Albion

68

Figure 5-2. The mean number of mite eggs per strawberry trifoliate throughout the


0

20

40

60

80

100

120

140M

ean

# o

f eg

gs/t

rifo

liate


69

Figure 5-3. The mean number of mite motiles per strawberry trifoliate throughout the


0

1

2

3

4

5

6m

ean

# o

f m

ites

/tri

folia

te


70

Figure 5-4. The mean number of mite eggs per strawberry trifoliate throughout the


0

5

10

15

20

25

30

35M

ean

# o

f eg

gs/t

rifo

liate


71

CHAPTER 6

CONCLUSIONS

This study suggests that lab based spectroscopy and digital image analysis have

utility in detecting the presence of twospotted spider mites (TSSM) (Tetranychus

urticae). Linear regression models based of green-red vegetation index (GRVI) values

and visible atmospherically resistant index (VARI) values, were significant for three of

the varieties (‘Albion’, ‘Winterstar’, and ‘Radiance’), and showed the expected negative

relationship between GRVI values and mite numbers. Regression models of normalized

difference vegetation index (NDVI) values, were also significant for three of the

strawberry varieties (‘Florida Festival’, ‘Winterstar’, and ‘Radiance’). These models,

however, did not adequately describe the relationship between single band reflectance

values and vegetation indices for all varieties equally suggesting varietal differences in

the ability to use single reflectance bands and vegetation indices derived from simple

digital imagery as a means to detect varying level of TSSM infestations. More

vegetation indices should be evaluated for their ability to predict different population

levels of TSSM, and more varieties should be investigated to further elucidate the total

applicability of this simple image analysis approach. Larger sampling regiments should

be developed, and confounding factors such as plant nutrition, water, and

simultaneously occurring pest pressures should also be examined to develop more

inclusive and powerful models. In addition to this, future research could utilize more

advanced imaging techniques, such as artificial neural network analyses as well as

machine learning classification models to better classify and predict TSSM infestation

levels by characterizing a higher number of fine scale spectral, and structural nuances

captured in images of strawberry plants.

72

Feeding by different population levels of twospotted spider mites did have some

differential effect on the spectral response of strawberry leaves as determined by full

spectrum, VIS+NIR, and VIS partial least squares regression models, yet these models

was deemed ‘poor’ in their predictive ability due to a high root mean square error of

prediction that is beyond the range of acceptability for practical applications. Future

research will utilize a higher number of samples to develop calibration and prediction

datasets that are more representative of the entire range of mite infestation levels, and

varieties will be modelled separately in order to observe varietal differences like those

observed in the imagine portion of the study. While the calibration model fit the data

well, it is not entirely possible to say whether or not the model captured underlying latent

predictors of mite infestation, or simply modeled apparent changes in wavelengths

between samples.

In this study, there was an observed difference between the population of TSSM

in ‘Florida Festival’ and ‘Albion’ strawberries, though it cannot be concluded whether or

not one of the varieties is more susceptible to TSSM infestation, based on the

observational nature of the study. This does however raise the question of the

difference in varietal susceptibility between ‘Florida Festival’ and ‘Albion’ strawberries,

and future research should focus on establishing controlled experiments to determine

these differences.

The results of the imaging portion of this study are promising in that inexpensive

and easy to implement imaging system could potentially be implemented to decrease

the burden of traditional TSSM monitoring practices. Future analyses could focus on

additional biometric parameters indicative of infestation, such as canopy and leaf

73

structure, and could also focus on automation for easy implementation in the field by

growers. More extensive and detailed spectroscopic techniques could continue to be

utilized in conjunction with readily available imaging solutions to inform the development

of camera technologies focusing on wavelength regions that are important for the

detection of TSSM. Overall the technologies presented in this study are promising in

their future applications of improving upon traditional, labor intensive monitoring

techniques for arthropod pests.

74

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78

BIOGRAPHICAL SKETCH

Christopher Crockett was born in Charleston, South Carolina, USA. He attended

Virginia Commonwealth University (VCU), where he received a Bachelor of Science in

biology, and a Bachelor of Science in environmental studies. During his time at VCU,

Christopher worked on farms in Cork, Ireland; Joensuu, Finland; and Richmond,

Virginia. During his undergraduate degrees he performed research on olfactory

conditioning, and its effect on parasitoid behavior with Dr. Karen Kester. Upon

graduating from VCU, Christopher joined the Small Fruit and Vegetable IPM Laboratory

at the University of Florida in order to complete a Master of Science (MS) in Entomology

degree under the guidance of Dr. Oscar Liburd. His MS research focused on the use of

remote sensing techniques to monitor twospotted spite mites on strawberries,

specifically the use of ground-based digital imagery, and leaf spectroscopy. He received

his MS degree in entomology in August 2015, and will be pursuing a PhD in the fall of

2015, focusing on the use of aerial imagery as a means to detect and monitor arthropod

damage in crop systems.

Documents

EVALUATION OF IN SITU DETECTION METHODS FOR …evaluation of in situ detection methods for twospotted spider mites (tetranychus urticae koch) in strawberry by christopher d. crockett