Added value of plant height and multi-temporal modelling to improve
the estimation of wheat traits based on proxy-sensing RGB cameras.
Dandrifosse S., Carlier A., Bustillo Vazquez E., Bouvry A.,
Leemans V., Dumont B. and Mercatoris B.
NSABS
2020
This study is funded by the Walloon Region
FRIA grant + project D31-1385 « PhenWheat »
Introduction
Methods
Results
Conclusions
2
Measure growth and health status of crops using RGB camera
Non destructive
Automatic
Objective
Affordable
Introduction
Methods
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4
1. Record height
Observed
zone
2. Use images at time t-1
Height
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Goals
I. Quantify added value of
height information…
II. Quantify added value of
multitemporal modelling…
…to estimate key target traits :
LAI
Biomass
Nitrogen
Introduction
Methods
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6
Trial field
• Location : Lonzée (Gembloux)
• Variety : Safari (250 seeds/m²)
• Sowing : 17-10-2018
• Soil : loamy, moderate drainage
• 4 replications
Data acquisition at 3 dates
176
194
216
Days after
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Image acquisition
Reference measurements
• Dry matter
• LAI
• Nitrogen
• BBCH growth stage
• 2 RGB cameras : JAI USB-GO5000
• Cameras spaced from 50 mm
• 1 m nadir above crop
Introduction
Methods
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RGB image
Segmented image
Background
Plants
Height map
Introduction
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Conclusions
9
LAI
Biomass
Nitrogen
PLS regression
10-k cross-validation
Height
Color
Cover
Time t-1
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GOAL I.
Added value of height
Total biomass (t/ha) Leaf biomass (t/ha) Nitrogen (kg/ha) LAI (m²/m²)
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GOAL II.
Added value of
images at t-1
Total biomass (t/ha) Leaf biomass (t/ha) Nitrogen (kg/ha) LAI (m²/m²)194
216
Introduction
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194
176
Introduction
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13
Height : SIGNIFICANT added value
Thank you for your attention
Any questions ?
References
• Baresel, J. P., Rischbeck, P., Hu, Y., Kipp, S., Hu, Y., Barmeier, G., & Mistele, B. (2017). Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture, 140, 25–33.
• Fernández, E., Gorchs, G., & Serrano, L. (2019). Use of consumer-grade cameras to assess wheat N status and grain yield. PLoS ONE, 14(2), 1–18.
• Jia, L., Chen, X., Li, M., Cui, Z., & Zhang, F. (2009). Comparsion of multispectral reflectance with digital color image in assessing the winter wheat nitrogen status. IFIP International Federation for Information Processing, 294, • Michez, A., Bauwens, S., Brostaux, Y., Hiel, M. P., Garré, S., Lejeune, P., & Dumont, B. (2018). How far can
consumer-grade UAV RGB imagery describe crop production? A 3D and multitemporal modeling approach applied to Zea mays. Remote Sensing, 10(11).
• Prey, L., von Bloh, M., & Schmidhalter, U. (2018). Evaluating RGB imaging and multispectral active and hyperspectral passive sensing for assessing early plant vigor in winter wheat. Sensors (Switzerland), 18(9).
• Tavakoli, H., & Gebbers, R. (2019). Assessing Nitrogen and water status of winter wheat using a digital camera.
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Fertilization modalities
and associated 2019 grain
yields
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RGB image
Segmented image
Height map
PREDICTORS CC : Canopy Cover ---VI : 9 Vegetation indices ; ; H : 4 Height predictors • Mean • Median • Percentile 95 • Standard deviation
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Segmentation method : Excess Red (ExR) threshold
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Add BBCH stage
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DM (t/ha) DM leaves (t/ha) N (kg/ha) LAI (m²/m²)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.61 0.77 0.75 0.82 0.9 0.78 0.54 0.64 0.16 0.31 0.32 0.39 0.14 0.37 0.48 0.55 0.19 0.42 0.51 0.58 CC Havg R G B R²
