Machine-learning models for the real-time assessment of plant health using UAVs and RGB images

Abstract

This paper presents the development of machine learning models and on-site infrastructure development for real-time plant health assessment in real-time using the images collected from unmanned aerial vehicles (UAVs). The data used for the training includes RGB images and spectral data collected from different UAVs equipped with RGB camera and multispectral sensors. Fully convolutional neural network (CNN), U-Net v2 was used to train the machine learning models. Statically defined geolocations extracted from the rectified rasters were augmented to efficiently generate a large dataset. The trained model was then used to interpret a greater variety of plant health information from just the RGB images. The paper discusses the development of a machine learning models to provide accurate, real-time, and actionable plant-level health information and eliminate the complexities involved with processing high density spectral data. The paper also discusses the development of stationary and mobile hardware required for real-time assessment of plant health.