Abstract
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.
Highlights
Farmers use soil plant analysis development (SPAD) devices as a field diagnostic tool to estimate nitrogen (N) content in the plant and to predict grain yield [1,2]
The algorithm provides to the user with the option to manually select these points in order to remove them from the segmentation process
We present a comprehensive comparison among multi-variable linear regressions (MLR), support vector machines (SVM), and artificial neural networks (NN) for the estimation of the canopy N
Summary
Farmers use soil plant analysis development (SPAD) devices as a field diagnostic tool to estimate nitrogen (N) content in the plant and to predict grain yield [1,2]. Using SPAD devices for crop diagnosis is still time consuming. With the advent of low-cost unmanned aerial vehicles (UAVs), several authors have reported faster and accurate remote sensing tools and methods [6,7] to estimate the N canopy from aerial multispectral imagery [8,9]. One of the most common methods used, relies on sensing the canopy reflectance in both visible (VIS) and near-infrared (NIR) wavelengths, using hyperspectral sensors [10,11,12,13]. 20 40 60 80 100 Imagery samples (3) Machine-Learning MLR SVM NN (d)
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