Evaluating the capabilities of Sentinel-2 and Tetracam RGB+3 for multi-temporal detection of thrips in Capsicum

Various pests and diseases can deteriorate the quality and yield of the capsicum. In order to control these losses, their timely detection is important. Thrips is one of the major pests in capsicum which is unable to detect in initial phase as the symptoms are not visible to naked eyes. Thrips not only causes plant damage but for the serious plant diseases it vectors. In this paper, we address the problem of detection of low infestation of thrips on capsicum leaves using multi-temporal hyperspectral remote sensing data simulated to multispectral sensors such as Sentinel-2 and Tetracam RGB+3. The reflectance data from capsicum leaves with healthy and low infestations of thrips has been collected using handheld spectroradiometer. The hyperspectral remote sensing data is collected from 213 bands with wavelength ranging from 350 nm to 1052 nm and bandwidth varying from 3.22 nm to 3.346 nm during the period of 17 Mar to 13 Apr 2017. Variations observed in the spectral reflectance over time makes the detection based on multi-temporal data difficult. We have evaluated the performance of tuned random forest classifier for various set of features such as full feature set of 213 bands, features selected by Least Absolute Shrinkage and Selection Operator (LASSO) from 213 bands, features simulated to broad bands similar to Sentinel 2 and features simulated to multispectral bands similar to Tetracam RGB+3 (a camera which can be placed on drones). Results suggests that an overall classification accuracy of 92.81 % has been achieved on validation dataset using full feature set whereas accuracy slightly dips down to 90.3, 85.13 and 87.45 % when using selected features by LASSO, bands simulated to Sentinel-2 and Tetracam-RGB+3 respectively. Results imply that, Tetracam-RGB+3 and Sentinel-2 satellite can be effectively used for detection of low-infestation of thrips on capsicum.