Crop scouting using UAV imagery: a case study for potatoes

2020 ◽  
Vol 8 (2) ◽  
pp. 99-118
Author(s):  
Jérôme Théau ◽  
Erwan Gavelle ◽  
Patrick Ménard

Crop scouting is essential to manage crops such as potatoes and to detect stresses. In fact, conventional approaches require a lot of time and staff. The rise of unmanned aerial vehicle imagery offers interesting perspectives in this area. However, the development of interpreted and generalizable cartographic products that can be used directly by producers still poses challenges in terms of processing complexity and production time. The purpose of this study was to develop a tool for the phytosanitary surveillance of potato crops using scouting maps in support of conventional methods. The approach is based on relatively simple and efficient preprocessing methods when no radiometric correction data are available. The first step establishes the best correlations between some biophysical parameters directly related to phytosanitary problems and vegetation indices in the visible and infrared domains. The second step allows the development of an approach to classify the following three stresses: pests, diseases, and development problems. Validated by scouting field sites, the developed approach makes it possible to quickly produce accurate scouting maps that producers can use directly thanks to their high potential for generalization to other areas and crop productions.

2018 ◽  
Vol 130 ◽  
pp. 636-643 ◽  
Author(s):  
Muhammad Arsalan Khan ◽  
Wim Ectors ◽  
Tom Bellemans ◽  
Yassine Ruichek ◽  
Ansar-ul-Haque Yasar ◽  
...  

Aerospace ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 23 ◽  
Author(s):  
David Communier ◽  
Ruxandra Mihaela Botez ◽  
Tony Wong

This paper presents the design and wind tunnel testing of a morphing camber system and an estimation of performances on an unmanned aerial vehicle. The morphing camber system is a combination of two subsystems: the morphing trailing edge and the morphing leading edge. Results of the present study show that the aerodynamics effects of the two subsystems are combined, without interfering with each other on the wing. The morphing camber system acts only on the lift coefficient at a 0° angle of attack when morphing the trailing edge, and only on the stall angle when morphing the leading edge. The behavior of the aerodynamics performances from the MTE and the MLE should allow individual control of the morphing camber trailing and leading edges. The estimation of the performances of the morphing camber on an unmanned aerial vehicle indicates that the morphing of the camber allows a drag reduction. This result is due to the smaller angle of attack needed for an unmanned aerial vehicle equipped with the morphing camber system than an unmanned aerial vehicle equipped with classical aileron. In the case study, the morphing camber system was found to allow a reduction of the drag when the lift coefficient was higher than 0.48.


2020 ◽  
Vol 12 (13) ◽  
pp. 2071
Author(s):  
Hwang Lee ◽  
Jinfei Wang ◽  
Brigitte Leblon

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by the farmer. The drawbacks of N deficiency on crops include poor plant growth, ultimately reducing the final yield potential. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery to predict canopy nitrogen weight (g/m2) of corn fields in south-west Ontario, Canada. Simple/multiple linear regression, Random Forests, and support vector regression (SVR) were established to predict the canopy nitrogen weight from individual multispectral bands and associated vegetation indices (VI). Random Forests using the current techniques/methodologies performed the best out of all the models tested on the validation set with an R2 of 0.85 and Root Mean Square Error (RMSE) of 4.52 g/m2. Adding more spectral variables into the model provided a marginal improvement in the accuracy, while extending the overall processing time. Random Forests provided marginally better results than SVR, but the concepts and analysis are much easier to interpret on Random Forests. Both machine learning models provided a much better accuracy than linear regression. The best model was then applied to the UAV images acquired at different dates for producing maps that show the spatial variation of canopy nitrogen weight within each field at that date.


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