Remote sensing enabled sustainable tomato plant health and pest surveillance using machine learning techniques

To ensure long-term food security, it's vital to detect pests and diseases in crops. Traditional monitoring methods are labour-intensive and lack real-time info, leading to ineffective interventions. To address this, an advanced plant health monitoring (APHM) system that uses remote sensing technology and machine learning-based classification to effectively identify plant diseases and pests in tomato plants is proposed. The proposed system allows unmanned aerial vehicles (UAV) to take detailed aerial images of tomato fields, which are processed and input into a convolutional neural network with a generating adversarial imitation learning (CNN-GAIL) model. The CNN-GAIL model efficiently differentiates between healthy plants, weak plants, and pest presence by capitalising on temporal relationships in image sequences. The APHM system's ability to quickly identify and fix problems, combined with remote sensing and machine learning in precision agriculture, could improve crop health, reduce yield losses, and promote long-term sustainability.