Robust two-stage object detection using YOLOv5 for enhancing tomato leaf disease detection

Abstract

Deep learning facilitates human activities across various sectors, including agriculture. Early disease detection in plants, such as tomato plant that are susceptible to diseases, is critical because it helps farmers reduce losses and control the disease spread more effectively. However, the ability of machine to recognize diseased leaf objects is also influenced by the quality of data. Data collected directly from the field typically yields lower accuracy due to challenges faced in machine interpretation. To address this challenge, we propose a two-stage detection architecture for identifying infected tomato plant classes, leveraging YOLOv5 to detect objects within the images obtained from the field. We use Inception-V3 for classifying objects into known classes. Additionally, we employ a combination of two dataset: PlantDocs which represent field data, and PlantVillage dataset which serves as a cleaner dataset. Our experimental results indicate that the use of YOLOv5 in handling data under actual field conditions can enhance model performance, although the accuracy value is moderate (62.50 %).