Wheat Canopy Cover Estimation by Optimized Random Forest and UAV Multispectral imagery

Abstract

Canopy cover estimation is widely applied to reflect crop status in agriculture research and management. In particular, an accurate CC estimation is beneficial for crop model calibration, providing high-accuracy observations. Recent solutions on CC are drawn by experimental regression or basic machine learning classifier because CC estimation can be treated as a wheat/non-wheat segmentation task. However, the appearance of hyperparameters in such machine learning algorithms impairs the segmentation performance. In this paper, by the means of UAV multispectral imagery, Bayesian optimization based Random Forest approach is selected to tune the uncertain hyperparameters accurately and robustly, providing a novel way on CC estimation. Experimental results collected in Yangling experiment field by the RedEdge camera on DJI M100 UAV are to evaluate the proposed method. Comparative studies show that the overall accuracy can reach up to 99.9%, promoting 0.2% in comparison with basic Random Forest. Therefore, integrating optimized Random Forest and UAV multispectral imagery can be applied in CC estimation at farmland scales.