DeepTrac: Applying Artificial Intelligence in Plant Disease Detection

Abstract

Small community local farmers face many unique issues in farming such as irrigating uneven farming lands, measuring soil water level, plant health monitoring, everyday trips to inspect for weed and pests, high cost and low-quality products, and many more. Most critical and challenging problem in farming is the early detection of plant diseases. In this research, an AI-Deep learning based automated plant disease detection method using FPN with Faster R-CNN architecture is proposed. In this work, we evaluated our proposed method on disease detection with main outcome measure of Intersection over Union (IoU) ratio (ratio of overlap between predicted and annotated bounding boxes), precision (ratio of true predictions to total predictions), recall (ratio of true predictions to annotated bounding boxes) and disease spread direction. Our method is experimented on Bacterial Spot detection in bell-pepper plant leaf images obtained from the PlantVillage dataset. The proposed model achieved average precision 100% and average recall 99.7% for an IoU ratio of 0.5; mean average precision was 99.5% and mean average recall was 99.1% for an IoU of 0.5-0.95. We then evaluated our DeepTrac module using video data which is then plot the disease spread directions to understand the pattern of bacterial spot spread in bell-pepper leaf. Our future work may include the application of our AI method to detect plant diseases on the drone-obtained plant images.