Upload
lemnatec
View
218
Download
1
Tags:
Embed Size (px)
DESCRIPTION
Features thatcanbemeasured: Phenodays, Wageningen October 13, 2011 Several examples focussing on image analysis 1 4 2 Vision system for sorting of orchids Gerie van der Heijden ([email protected]) Improved crops Contact: JochemHemming Contact: Rick vdZedde Contact: Rick vdZedde Crops Genotype……………………………...Phenotype Environmental variation Sequencing + Genotyping Ph 1 3 5
Citation preview
10/19/2011
1
Plant Phenotyping at Wageningen UR
Gerie van der Heijden ([email protected])
Phenodays, WageningenOctober 13, 2011
From Genotype to Phenotype
Improved crops
Genotype……………………………...Phenotype
Crops
Environmental variation
Seq
ue
nc
ing
+
Ge
no
typ
ing
Ph
1
42
3
5
Phenotyping at Wageningen UR
Several examples focussing on image analysis Robotics and automation of pot plants Time monitoring of Arabidopsis Chlorophyll fluorescence Hyperspectral imaging 3D imaging Analysis of complex plant scenes
Image analysis and automation
Vision system for sorting of orchids
Contact: Rick vd Zedde
Automatic harvesting of roses
Contact: Rick vd Zedde
Plantalyser system for pot plants
Features that can be measured: Plant height and width (generative
and vegetative) Projected area of leaf and flower
(side and top) Number and size of flowers Average colour of leaves and
flowers Leaf orientation Shoot density and width
Contact: Jochem Hemming
10/19/2011
2
Plantalyser
Virtually for all kind of potplants(max. plant height: 120 cm)
Wide range of features are tested and validated for:• Anthurium• Spathiphyllum• Kalanchoë• Curcuma
New species will require some tuning New features will require algorithm
development and programming
Monitoring Arabidopsis
Monitoring Arabidopsis
J. Kokorian, G. Polder, J.J.B. Keurentjes, D. Vreugdenhil, M. Olortegui Guzman. An ImageJ based measurement setup for automated phenotyping of plants. ImageJ Conference. 2010
Hyperspectral imaging
Acquisition of hyperspectral images Hyperspectral imaging of tomatoes
Tomatoes in different ripeness stadia
10/19/2011
3
Measurement of Lycopene content
G. Polder, G.W.A.M. van der Heijden, H. van der Voet, and I.T. Young. Postharvest Biology and Technology, 34(2):117–129,
2004.
HPLC measurement
Detecting Fusarium in wheat seeds
A. color reflection
B. NIR transmission at 1100 nm
C. Predicted Fusariumconcentration
Use of transmission NIR (900-1800 nm)
15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.513
14
15
16
17
18
19
20
21
--102
--104
--108
--110
--202
--203
--205--206
--208
--210
--211
--212
--301
--302--304
--305
--307
--308
--309
--310
--311
--312
--401
--403--407
--408
--409
--411
--412
--501
--503
--505
--507
--508--509
--510
--511
--512
--601
--602--603
--605
--606--610
--611--702
--703--706
--707
--708--710--711--802
--805--807--808
--809--810
--812
Pixel error:Object error:RMSEP: 0.82
RMSEP/: 0.04
Q2: 0.79
RMSEP: 0.79
RMSEP/: 0.04
Q2: 0.80
Detecting Fusarium in wheat seeds
Measured Ct
Pred
icte
d Ct
PLS with cross validation on seed kernel basis
G. Polder, G.W.A.M. van der Heijden, C. Waalwijk, and I.T. Young. Seed Science and
Technology. 33(3):655–668, 2005.
Hyperspectral imaging of grassland
velocity 0.3-0.5 m/s
1
2
3
4
5
1 2 3 4 5
Voorspeld N (%)
5
10
15
20
25
30
5 10 15 20 25 30
DS gehalte (%)
0
1
2
3
4
0 1 2 3 4DS opbrengst (ton ha- 1)
Predict biomass, DMC and N-content
DM yield (ton/ha) DM content (%) N content (%)
Mea
sure
d
Predicted
A.G.T. Schut, G.W.A.M. van der Heijden, I. Hoving, M.W.J. Stienezen, F.K. van Evert, and J. Meuleman. Agronomy Journal. 2006.
Combining remote and close sensingPhysical and
chemical measurement
Close sensing
Remote sensing
cal cal
10/19/2011
4
Biomass and nitrogen prediction in the field
Spatial pattern of the predicted biomass (tons/ha) for the grass/clover field
van der Heijden, G.W.A.M., Clevers, J.G.P.W. and Schut, A.G.T. International Journal of Remote Sensing, 28:24, 5485 – 5502. 2007.
CF Transient Imager
Contact: Henk Jalink
F0
Fmax
Fv/Fm = (Fmax– F0)/Fmax
Imaging the induction curve of photosynthesis
Quantification of stress in leaves
G. Polder, G.W.A.M. van der Heijden, H. Jalink and J. Snel. Computers and electronics in Agriculture, 55:1-15, 2007.
Time sequence cabbage plants
After movement correction we can monitor every pixel in time and early discern regions with decreased photosynthesis efficiency
10/19/2011
5
Control 6D Salt 13D Salt
Sensitive
TolerantTraits:
• Photosynthetic activity Fv/Fm• Distribution of Fv/Fmover plant
Find responsible genomicregion: QTL
Contact: Henk Jalink and Gerard vd Linden
Salinity stress in potato High-throughput 3D seedling sorter
Aim: Seedling assessment based on human
expert knowledge modelling
Approach: Highly accurate 3D reconstruction using
volumetric intersection of 10 camerassimultaneously
Result: a high-speed sorting device in cooperation
with Flier Systems BV.
High-throughput 3D-based seedling sorting
Capacity: 20.000 seedlings/ hour.
Processing time: 45ms per seedling
Contact: Rick van de Zedde
Plant Phenotyping in EU project SPICY
Combine phenotypic data with genotypic data and crop growth models for pepper
Partners: WUR (coordinator), INRA (FR), VIB (BE), James Hutton Institute (UK), Budapest Univ (HU), Exp. station Cajamar (ES)
QTLGenetic
params
Generic
params
Env.
params
Crop
Growth
Model
Pheno-
type
Recombinant inbred linesof pepper
Yolo Wonder CM334
genotyping 297 RIL
F5YC
- 530 markers (AFLP, RFLP, SSR, KG,...)assigned to 12 chr. (1500 cM)- 100 markers / 20 small LG (+ 300cM)
Genetic map
0,0
17,8
18,5
22,2
36,3
40,7
53,6
62,4
67,1
70,2
83,4
91,1
93,6
95,9
101,5
104,6
110,7
P2
0,0
30,8
38,6
42,4
44,5
49,3
56,4
63,4
70,1
76,6
80,9
85,3
90,6
93,8
102,0
111,8
148,2
156,3
161,1
170,4
172,9
P4
0,0
9,0
16,4
20,6
24,4
28,3
31,2
34,1
36,1
38,5
45,1
54,4
83,0
P7
0,0
10,3
13,2
18,7
35,2
40,0
45,3
47,5
52,9
55,7
86,7
P8
0,0
9,2
20,3
28,9
43,9
50,2
56,0
61,0
67,1
72,4
81,8
0,0
11,9
16,7
25,2
34,6
P100,0
7,8
14,7
19,9
25,6
0,0
19,6
28,8
31,7
33,0
35,3
38,0
41,5
45,1
46,8
49,6
52,8
56,1
60,7
67,9
71,1
74,0
74,8
78,5
81,4
90,3
97,6
104,8
117,5
124,4
P110,0
6,5
11,4
13,2
23,1
30,9
40,2
44,0
46,9
54,9
62,0
83,1
85,4
Frd12.1IM
P12P10,0
7,3
15,0
19,1
28,2
33,3
35,3
37,2
41,8
51,655,8
60,1
62,2
66,9
69,9
72,3
74,3
89,5
97,6
101,9
104,0106,1
108,5
112,3
114,2
116,7119,4
123,7
128,7
134,6
137,7
142,6
145,2
148,7
180,8
184,9
188,6
192,4
197,8
205,4
P50,0
7,3
15,0
19,1
28,2
33,3
35,3
37,2
41,8
51,655,8
60,1
62,2
66,9
69,9
72,3
74,3
89,5
97,6
101,9
104,0106,1
108,5
112,3
114,2
116,7119,4
123,7
128,7
134,6
137,7
142,6
145,2
148,7
180,8
184,9
188,6
192,4
197,8
205,4
P50,0
7,3
15,0
19,1
28,2
33,3
35,3
37,2
41,8
51,655,8
60,1
62,2
66,9
69,9
72,3
74,3
89,5
97,6
101,9
104,0
106,1
108,5
112,3
114,2
116,7119,4
123,7
128,7
134,6
137,7
142,6
145,2
148,7
180,8
184,9
188,6
192,4
197,8
205,4
0,0
2,8
12,6
24,5
32,2
38,7
44,4
48,1
51,0
53,3
57,8
60,4
62,5
65,4
68,3
70,7
74,8
P60,0
2,8
12,6
24,5
32,2
38,7
44,4
48,1
51,0
53,3
57,8
60,4
62,5
65,4
68,3
70,7
74,8
P60,0
8,8
13,7
41,4
55,6
76,7
90,4
93,4
100,1
107,0
108,3109,5
110,6
111,9
114,6115,7
119,2
121,2
124,3
128,3
132,1133,7
136,2
139,6
144,0
148,7
154,0
157,1
168,5
180,5
185,3
189,5
194,0
205,2
0,0
8,8
13,7
41,4
55,6
76,7
90,4
93,4
100,1
107,0
108,3
109,5
110,6
111,9
114,6115,7
119,2
121,2
124,3
128,3
132,1
133,7
136,2
139,6
144,0
148,7
154,0
157,1
168,5
180,5
185,3
189,5
194,0
205,2
P9P30,0
7,1
11,7
16,6
21,3
27,7
31,5
32,437,4
45,8
49,1
53,7
55,657,7
65,7
95,7
130,5
137,9
146,4
153,0
160,7
174,1
SPICY: Plant material and genetic map Phenotyping large pepper plants
Build high rig with: 4 RGB camera’s 4 infra-red camera’s 4 range camera’s Use wide angle lenses
Challenging: • high plants (3 m high)• row space small(< 60 cm)• intertwined plants
10/19/2011
6
Problem: varying lighting conditions System setup
Rig in greenhouseIR camera RGB camera Range camera (TOF)
Flash light Mirror
Record images every 5 cm
RGB image second row Infrared second rowQR barcode
Time-Of-Flight Range camera
A Time-of-Flight (ToF) camera can produce a depth image.
The camera illuminates the scene by infrared light.
Distance in cm is calculated from the time the light has used for travelling to the object and back.
Low resolution
Approach
Reconstruct 3D canopy (depth information)
Then extract features number of leaves size of leaves leaf area angle of inclination of leaves number and size of fruits stem thickness internode length ....
3D canopy reconstruction
Combine stereo color images and ToF range image by mapping them to a single 3D reference coordinate system
ToF image: (mixed) pixel represents a patch in 3D Integrate ToF image with stereo RGB images using
graph cuts (GC).
Yu Song, C. Glasbey, G.W.A.M. van der Heijden G. Polder and A. Dieleman. Combining stereo and
Time-of-Flight images with application to automatic plant phenotyping. SCIA. 2011.
10/19/2011
7
Depth estimation using stereovision
Disparity Depth
Pinhole camera model:
z = s f / d
Pre-defined parameters s and f
Automated estimation
Dense correspondence (depth for every pixel in image)
d1
d2
Coarse ToF image
Low resolution ToFimage
Combine with high-resolution RGB images
Different viewingposition and lens
Results
Depth estimation using 3 state-of-the-art stereo algorithms (SIFTflow,Shape,GC), using only ToF and using our method, combining ToF and GC.
RGB SIFTflow SHAPE GC ToF ToF+GC
Yu Song, C. Glasbey, G.W.A.M. van der Heijden G. Polder and A. Dieleman. Proceedings of SCIA. 2011.
Measuring leaf area
Now we have a 3D reconstructed scene, we can automatically extract a leaf from the scene and compute its surface area.
Smoothing of leaf surface Leaf area results
Leaf area (cm2) Plant 1 Plant 2 Plant 3
Manually measured 67.25 97.27 36.60
No smoothing 109.66 149.38 62.65
Smoothing 69.70 102.40 33.74
10/19/2011
8
High Throughput and Deep Phenotyping
genotyping depth full genome sequencing
when (development in time)
where (cell/tissue/organ)
conditions (different enviroments)
transcriptomics
standardmeasurements
VIS/NIR/UV/X-ray/NMR/...
proteomics
metabolomics
phen
otyp
ing
dept
hData explosion
We generate far too much data to handle manually Simple summary statistics as means and standard
deviations do not suffice Advanced analysis tools are required
An example: QTLxE analysis
P1 P2
DH n
3.4 4.4 3.4 …. 1.03.4 3.5 2.4 …. 2.03.1 3.5 2.6 …. 4.02.8 3.0 2.4 …. 3.0E
nvir
onm
ents
2004
2005
Population of e.g. Doubled Haploids, Genetic information
DH1 DH2 DH…
Barley
Environmental information: climate, soil
Phenotypic scores
Effects of QTL on chromosome
P1 Allele Superior
P2 Allele SuperiorNo significant effect
QTL-effects related to
temperature
Boer, M.P.; Wright, D.; Feng, L.; Podlich, D.W.; Luo, L.; Cooper, M.; Eeuwijk, F.A. van. Genetics 177 (3). - p. 1801 - 1813. 2007.
Thank you for your attention
© Wageningen UR
Contact: [email protected]