Estimation of Nitrogen in Rice Crops from UAV-Captured Images

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-10, Vol.12 (20), p.3396
• Main Authors: Colorado, Julian D., Cera-Bornacelli, Natalia, Caldas, Juan S., Petro, Eliel, Rebolledo, Maria C., Cuellar, David, Calderon, Francisco, Mondragon, Ivan F., Jaramillo-Botero, Andres
• Format: Article
• Language: English
• Subjects: Algorithms; Biomass; Calibration; Cameras; Cereal crops; Chlorophyll; Control theory; Crop growth; Crop production; Crops; Datasets; Experiments; Image processing; Image segmentation; Irrigation; Learning algorithms; Leaves; Life Sciences; Machine learning; Neural networks; Nitrogen; Perturbation; Photogrammetry; Remote sensing; Rice; Ripening; Sensors; Soil analysis; Support vector machines; Trajectory control; Unmanned aerial vehicles; Vegetation; Wind; Wind effects
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203396

Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections.