GeoSpatial Yield Monitoring Technology for Mechanized Almond harvest

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 4, 2011

© Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 – 4380

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GeoSpatial Yield Monitoring Technology for Mechanized Almond harvest

Balaji Sethuramasamyraja 1 , Sunny Sehgal 1 , Jim Yager 2 1 Department of Industrial Technology, California State University, Fresno

2255 E Barstow Ave, M/S IT 09, Fresno, CA 937408002, USA 2 Impact Marketing Enterprises & Hydroponic Mastery Inc.,

Fresno, CA 93740, USA [email protected]

ABSTRACT

Yield monitoring is critical for almond growers to garner profitability through application of geospatial technology and site specific crop management in almond production. Almond growers could optimize water and application of inputs based on soil thematic layer of the field that has a strong correlation to yield besides management practices. In this research a commercially deployable yield monitor was developed for almonds with sensing, global positioning system (GPS) and geographic information systems (GIS) technology (DigiStar® Yield Monitor). Yield maps were developed for production almond fields. The yield data was validated using sampling datasets which were in turn correlated to soil thematic layer developed using soil electrical conductivity (Veris® 3100 EC Mapper). The sampling from multiple soil zones representing a soil texture were correlated to the almond turn out ratio. Also, the effect of navel orange worm (NOW) on almond meat turn out ratio was reported.

Key words: Precision agriculture, precision viticulture, differential harvest, anthocyanin, wine grapes, geostatistics, kriging.

1. Introduction

Almond Orchards are ubiquitous in California and almonds tree nuts are a major part of Central Valley (Central California) agricultural production. California produces about 80% of world’s almonds and 100% of domestic need (USDA Market Report, 2009). In year 2009 California produced 1.35 billion lbs worth $2.34 billion almonds at an average 1900 lbs/acre and 108 trees/acre. There are over 6000 almond growers located in Central Valley of California alone. In 2009 the almond yield reduced by 20% from the previous year (USDA Acreage Report, 2009). Almond yield variation from year to year could be caused by several factors. However, soil variability is one critical factor that affects yield, especially if the grower’s management practices are not in line with the variability (Sethuramasamyraja et al., 2010). Although almond production in California is huge amounting to 0.9 million acres in 2009 (USDA Acreage Report, 2010), there is no usage of yield monitoring technology to capture an aspect of variability. Spatial variability is common in any field due to several reasons including natural terrain, management of land over the years and excessive or



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improper usage of agricultural chemicals. Almond orchard growers have realized this for decades and have devised several cultural management practices to overcome spatial variability. However, without proper application of technology and its related science, fruits of sitespecific management may not be realized profitably. The application of precision farming and geospatial technologies (Pierece and Nowak, 1999) to almond farming is relatively recent, but could take advantage of technologies such as yield monitoring applied in other crops like corn and soybean. The variability in yield revealed by this technology in several crops, although expected, is quite informative for precise management. As compared to the application of sitespecific crop management practices in row crop farming, application of geospatial technologies in almonds, by all means, adds considerable value due to the higher value of crops involved. For instance, differential harvesting of almonds based on soil variability has the potential for profitability in almond production (Sethuramasamyraja et al., 2007). Yield is correlated to soil texture, irrigation and agro chemical application (Maja and Ehsani, 2010). Sandy soils are typically low yielding soils due to the coarser particle size and hence its inability to hold nutrients and water for longer period. Clay soils on the contrary are high yielding soils due to the fine particles size as they could hold water and nutrients longer yielding higher yield (Shein, 2009). However, over irrigating in clayey soils could result in water clogging causing growth of mold on the crop due to excessive moisture resulting in pest incidence and crop damage.

Although adoption of geospatial technology in agriculture has been significant in the country, it has been relatively slow in the Central California region due to the diversity of crops and necessity of multiple mechanized systems in production. However, the initial investment in technology could be retrieved through proper application of technology in agriculture. There is no successful commercial almond yield monitor at this point and such a system will capture the almond yield variability. This variability could be the trigger for further analysis and comparison with other thematic layers like soil to determine the productivity on a site specific basis. Therefore, yield monitoring helps understand variability, which can be used for the application for sitespecific inputs, which is critical for protecting the quality of environment and attain profitability through optimization. The objectives of this project are to develop a sensor based yield monitoring system for mechanized almond harvest, create a geospatial map of almond yield, and validate almond yield variability with soil electrical conductivity.

2. Materials and Methods

2.1 Field Information

Two different almond fields were utilized in this research. The almond fields were Field I, Fargovsa Almond Farms located in West Fresno, California (Figure 1a & b) and University Agricultural Laboratory, California State University (CSU), Fresno, California (Figure 2a & b). Table 1 lists field specification.



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Table 1: Experimental Almond Field Specifications

Field Acres & Tree/ac

Rows & Ave. Trees Heading

Spacing (Row, Trees)

Field I (West Fresno, CA) 17, 123 63, 35 EastWest 20, 17

Field II (CSU, Fresno, CA) 57, 132 137, 55 NorthSouth 20, 15

Figure 1a: Fargovsa’s Almond Field with elevation (Field I), West Fresno, CA 1b. – Almond Trees in Field I (Image Courtesy: Google Earth 6.0)



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Figure 2a: University Agricultural Laboratory Almond Field (Field II), Fresno, CA with Soil Electrical Conductivity (EC)

Figure 2b: Almond Trees in Field II (Image Courtesy: Google Earth 6.0)

2.2 Sensors and Instruments

Table 2 lists the sensors, global positioning system (GPS) and software utilized in this research. The Trimble Juno ST is a hand held (Trimble Navigation Limited, Sunnyvale, CA) GPS systems, and has an integrated GPS receiver with Microsoft Compact Edition (CE) based mobile device. FarmWorks View (FarmWorks, Hamilton, IN) was used for mapping the yield and soil conductivity on Fargovsa’s farm (Field I). ArcGIS 9.3 desktop software (ESRI, Redlands, CA) was used for mapping the turnout percentage of almonds and soil conductivity on University Agriculture Laboratory, California State University, Fresno, CA (Field II). Digital Weighing scale (CenTech®) was used for weighing the samples collected from Field II. Irwin curved jaw locking plier (Irwin® Tools, Wilmington, OH) was used for hulling and shelling almonds.



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Table 2: Sensors, Instrumentation and Software utilized in experiments

Almond Farm Sensor User

Interface GPS Software

Field I (West Fresno, CA)

DigiStar Compression Transducer 3

DigiStar NT460 3

Trimble Juno ST Handheld 1 , Garmin GPS Antenna Kit 4

Farm Works View 5

Field II (CSUFresno, CA)

NA Trimble Juno ST Handheld 1

ESRI ArcGIS 9.3 2

1 Trimble Navigation Limited, Sunnyvale, CA 2 Environmental Systems Research Institute, ESRI, Redlands, CA 3 DigiStar, Fort Atkinson, WI 4 Garmin, Olathe, KS 5 FarmWorks, Hamilton, IN

Table 3 lists the operations on Field I and Field II. GeoReferencing and Soil Electrical Conductivity dataset was collected in both Field I and II. Field I was yield mapped while Field II was used for yield validation study.

Table 3: Field Operations in Field I and II

Operation Field I Field II GeoReferencing Yes Yes

Soil EC Yes Yes 1 Yield Mapping

2 Turnout Mapping Yes 1 Yes 2

2.3 Varietal Information & Yield Validation Data Collection

Table 4 lists the varietals planted in Field I and II with their attributes and applications (Almond Board, 2010). Butte and Padre were the varieties sampled from Field I. Nonpareil, Aldrich, Price, Butte and Padre were the varieties sampled from Field II.

Table 4: Almond Varietals

Group Varietal Classification Attribute Uses Nonpareil Nonpareil High Turnout Aldrich Nonpareil Wrinkled

Attractive appearances

Group I Price California High Percentage of

doubles Manufactured products

Group II Butte Padre

California & Mission

Versatile Kernel application

Salt and Flavor adherences



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Table 5 lists varietal sampling information including number of rows, trees & soil zones.

Table 5: Data Collection

Almond Farm Varietals Rows Trees Soil Zones Field I (West Fresno, CA) 2 63 2100 5 Field II (CSU, Fresno, CA) 5 16 88 8

5 almonds varietals were planted in Field II. In case of Group I random samples were collected from 6 different soil zones (10, 13, 31, 19, 25 & 22) with unique soil textures. From each zone 4 trees were selected, collecting 100 nuts from each tree. For Group II, samples were collected from 2 soil zones (7 & 55) and from each zone, 4 trees were selected collecting 100 nuts from each tree. Collected samples were paper bagged separately with each bag labeled with a distinct identification.

After sample from each zone were collected they were hulled to remove the green outer soft covering and were dried under sun for 48hrs to remove moisture and avoid mold damage. Dried samples were then shelled using the almond shell cutter and the shells and hulls were stored in separate bags. The meats were categorized such as wholes (without damage), shriveled (having wrinkles or are warped), stained, doubles (exactly two meats), aborted (no meat) and NOW (pest infested). The wholes were stored in a ziplock bag and the shriveled, stained, and doubles were stored in one zip lock bag while the almonds with navel orange worms and aborted almonds were store together. The almonds diseased with NOW were frozen overnight to avoid navel orange worms growth further.

2.4. Yield Monitoring System and Data Collection

Almond is typically harvested using a 3step process (Almond Board, 2010) – shaking, sweeping and picking (Figure 3). Prior to harvest, almond field was cleared off to remove any debris such as the tree branches and leaves left over in the field. A mechanical fork shaking system was utilized to shake the almond tree by holding on to almond tree trunk with a displacement and frequency of 10 mm. at 20 hz. The mature almonds fall in the ground during the shaking process which is then raked as windrows using an almond sweeper. The almonds were sundried on the ground for two weeks to remove excess moisture. Then an almond yield picker implement was used to pick the dried up almonds and shipped for further processing.

Yield monitoring systems are widely adopted in other crops including grains, fruits, vegetables and nuts although none are available for almond commercially (Shearer et al., 1999). A yield monitoring system (Figure 4) was developed using a combination mechanical load cell mounted on the axle of a yield implement, a GPS receiver for geo referencing of each load cell yield data point, a controller and user interface with data storage (Figure 5).



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Figure 3: Three Step Almond Harvest – Shaking, Sweeping and Picking

Figure 4: Almond Implement with the developed Yield Monitoring System

Figure 5: Yield Monitoring System in Mechanized Almond Yield Implement



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The compression transducer load cells (Figure 6a) mounted on each side of the axle of the yield implement measures the yield of picking using the principle of iterative measure. The load cells have a range of up to 50,000 lbs for measuring almond weight with an error range of 001 – 0.25 % that could be weighed in the storage chamber of the implement (Figure 6b).

Figure 6a: Almond Storage Chamber 6b. Load Cells & in Axle Mount 6c. Garmin GPS

6d. DigiStar NT 460 User Interface

Figure 7: Yield Monitoring System Data Stream



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A software program was developed for the controller to achieve the differential weighting scheme typical of almond picking onthego using differential algorithm. While each of the two, load cell data from the axle of the implement is averaged for a single yield value; the GPS receiver (Figure 6c) obtains an alphanumeric data string from satellites with the location information. The GPS string was decoded using the same controller program to add the northing and easting values of the coordinate in the yield log file along with the mean yield value in the user interface data storage (Figure 6d) as illustrated (Figure 7). The frequency of data collection was 0.33 Hz (one yield data point every three seconds) and the average mean distance between subsequent data points was 10.8 ft at an average speed of 2 mph. The above parameters could be adjusted in the user interface using inputs to the software program.

2.5. Soil Electrical Conductivity

Soil Electrical Conductivity (EC) is the measure of soil conductivity measured by sending an electrical signal, through a pair of electrodes, and receiving it in another pair of electrodes to determine the soil electrical conductivity. In the Veris soil EC Mapper, odd numbered electrodes 1, 3 and 5 injects an electrical signal into the soil and are subsequently received by their corresponding even numbered 2, 4 and 6 electrodes measures the drop in voltage to determine soil EC (Figure 8a). Two pairs of electrodes were utilized in the Veris 3100 (Veris®, Salina, KS) EC Mapper (Figure 8b) to collect soil electrical conductivity at two different depths (1 ft. and 3 ft as shallow and deep EC). The Soil EC is coupled with a GPS receiver that records the geographical coordinates creating a georeferenced map of soil shallow and deep soil EC.

Figure 8 a: Soil EC Principle b. Veris Soil Ec Mapper in Field II, CSU Fresno, CA



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3. Results and Discussion

3.1 Field I

The almond yield map shows the yield variability in Field I as mapped by the yield monitoring system developed (Figure 9).

Figure 9: Almond Yield Mapped using the developed Yield Monitoring System, Field I

Table 6 lists the yield information of two varietals, Group II with respect to the specific soil zones and their texture. It is apparent that the lowest almond yield was in soil zone 6 with sandy loam texture while the highest almond yield was in soil zone 1 that was clay loam.

Table 6: Group II Almond Varietal Yield, Field II

Soil Zone

Padre Yield (lbs/acre)

Butte Yield (lbs/acre)

Soil Texture

1 2523 1659 Loam 2 2077 2091 Sand Clay Loam 3 1961 1594 Sandy Loam 4 1739 1367 Sandy Loam 5 2062 1475 Sandy Loam 6 1218 1852 Sandy Loam

The yield map also demonstrates considerable variability in yield ranging from 1200 to 2500 lbs/ac. The soil EC dataset collected in Field I is overlaid to classify the soil zones. The statistical single tailed paired ttest of the varietals, padre and butte dataset yield in



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significant difference (P = 0.02 at 95% Confidence Interval) if soil zones 15 are considered while the same test results in insignificant difference if zone 6 is included (P>0.05 at 95% Confidence Interval), which may perhaps be the result of an outlier or unknown interaction.

3.2 Field II

Table 7 lists the turnout percentage (validated yield indicator) of almond meat from the wholes for field II in different soil zones. Apparently, the turnout is higher in loamy soil zones as compared to sandy soil zones, as illustrated by soil zone 22 with high meat turn out (5.6%) in loamy soil vs. soil zone 13 with lower meat turnout (2.2%) in sandy soil.

Figure 9: Soil Electrical Conductivity (EC) collected using Veris System, Field II

Similarly, lighter soil zone 10 has lower turnout (2.7%) as compared to heavier soil zone 25 (5.0%). It is apparent that soil zone 10 and 13 are completely sandy and soil zone 25 and 22 are sandy loam zone, which is a combination of sand, silt and clay (Figure 10). The statistical single tailed paired ttest of Nonpareil vs. Aldrich yield significant difference (P = 0.049 at 95% Confidence Interval) signifying their difference in potential yield. Table 8 lists the relationship between: soil zones, varietal and turn out percentage. It is inferred that the varietal’s turnout is unique based on soil zones. In soil zone 7, Padre



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turnout is relatively higher than Butte although they are in the same soil zone, which is a heavy loamy sand, as compared to soil zone 55 (Figure 12).

Table 7: Nonpareil, Aldrich, Price Almond varietal Yield Turnout Sampling Dataset

Average Weight (g)

Ave. Turnout

% (Soil Zone)

Ave. Count Soil Zone & Texture

Varietal

Whole Meat

Ave Turnout

% (Varietal

)

Std. Dev Turnout

%

Wh ole

Shriv eled

Dou bles

Nonpareil 701 27 3.7 * 3.12 37 20 2 Aldrich 441 16 3.6 * 2.15 30 25 4 10

(Sandy) Price 643 6 0.9 *

2.7 0.75 4 74 4

Nonpareil 726 18 2.3 * 2.04 23 43 3 Aldrich 433 13 2.8 * 1.56 26 38 4 13

(Sandy) Price 616 9 1.4 *

2.2 0.3 7 75 4

Nonpareil 633 11 2.2 1.3 8 45 2 Aldrich 366 21 5.8 1.25 19 27 6

31(Loamy Sand) Price 462 15 3.2 0.9 12 67 8

Nonpareil 752 21 2.8 3.7

0.98 12 39 1 Aldrich 435 19 4.5 1.62 16 32 6

19(Loamy Sand) Price 658 25 3.7 0.85 17 61 7

Nonpareil 842 37 4.4 * 4.2

0.67 22 46 3 Aldrich 432 23 5.6 * 1.44 19 38 7

25(Sandy Loam) Price 550 34 6.1 * 0.5 25 50 10

Nonpareil 766 25 3.2 * 5

0.29 14 22 1 Aldrich 431 22 5.1 * 1.25 19 41 5

22(Sandy Loam) Price 507 31 6.1 *

5.6 0.87 25 61 6

Table 8: Butte and Padre Almond Varietal Turnout on Soil Zone 7 & 5

Ave. Weight Soil Zone Varietal

Whole Meat Turnout %

Ave. Turnout Std.

Dev. Butte 532 44 8.3 * 2.2

7 Padre 550 56 10.1 * 0.8 Butte 536 60 11.2 1.5 55 Padre 672 46 6.8 1.6



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Figure 11: Nonpareil (N), Aldrich (A), Price (P) Almond Varietal Yield Turnout on Soil

Zones 13, 13, 25 & 22

Figure 12: Butte and Padre Almond Varietal Turnout on Soil Zone 7



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4. Conclusions

An almond yield monitoring system with sensing and global positioning system technology was developed and demonstrated in production fields. The yield mapped using the yield monitor system established the field yield variability that could potentially be taken advantage by geospatial (global positioning system, GPS) and geographical information system, GIS) and precision agricultural technologies to manage on a site specific basis optimizing inputs and protecting the quality of the environment. Almond yield turnout, yield indicator dataset was also collected to validate the almond yield variability with respect to the soil texture (soil electrical conductivity, EC). Five different almond varietals were utilized to study their performance in different soil zones as encountered in the two experimental fields in Central California region.

Acknowledgements

Support for this research was provided by Dr. Jim Yager (ImpactAg, Fresno, CA), Mr. Gino Fargovsa (Fargovsa Farms, West Fresno), and Department of Industrial Technology (California State University, Fresno). Partial funding was provided by our industry sponsor DigiStar, Fort Atkinson, WI.

5. References

1. Trees Nuts: World Markets and Trade (2009), United States Department of Agriculture, Washington, DC, USA.

2. California Almond Acreage Report (2009), United States Department of Agriculture, Washington, DC, USA.

3. B Sethuramasamyraja, H. Singh, and G. M. Mazhuvancheriparambath (2010), Geospatial modeling of wine grape quality (anthocynin0 for optimum sampling strategy in mechanized onthego differential harvesting programs, International Journal of Engineering Science and Technology, 2(10), pp 60566071.

4. Almond Lifecycle and Growth. (2010), Almond Board of California, Modesto, CA, USA.

5. California Almond Acreage Report (2010), Almond Board of California, Modesto, CA, USA.

6. F. J. Pierce, and P. Nowak. (1999), Aspects of precision agriculture. Advances in Agronomy, 67: pp 185.

7. B Sethuramasamyraja, S. Sachidhanantham, R. L. Wample, and M. Yen (2007), Interpolation of wine grape quality indicators (Anthocyanin and Brix) and development of differential harvest attachment. Paper No.071097. St. Joseph, Michigan: ASABE.



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8. J Maja, J., & R. Ehsani (2010), Development of a yield monitoring system for citrus mechanical harvesting machines [electronic resource]. Precision agriculture, 11(5), pp 475487.

9. E Shein (2009), The particlesize distribution in soils: problems of the methods of study, interpretation of the results, and classification. Eurasian Soil Science, 42(3), pp 284291.

10. Almond Varieties and Classification. (2010), Almond Board of California, Modesto, CA, USA.

11. From Orchard to Table, (2010). Almond Board of California, Modesto, CA, USA.

12. Shearer, S.A., Fulton, J.P., McNeill, S.G., Higgins, S.F., and Mueller, T.G. (1999), Elements of Precision Agriculture: Basics of Yield Monitor Installation and Operation, PA1, Kentucky Cooperative Extension Service, University of Kentucky, Lexington, KY.

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GeoSpatial Yield Monitoring Technology for Mechanized Almond harvest