Abstract:
Tomatoes are one of the major horticulture crops in the world. Early Blight is one of the most widespread tomato diseases in India, often causing a significant reduction in produce. Agricultural produce of tomatoes is of utmost importance, making it necessary for timely recognition of Early Blight. Using self-collected images, we first explore classification of Early Blight in diseased leaves using ResNet and Xception networks, achieving a classification accuracy of 99.952%. However, significant focus has already been dedicated to disease classification in crops. Additionally, the lack of spatial information for affected leaves persuades us to move towards an object detection approach, utilizing variants based on the YOLO framework. We illustrate results with a twin focus on accuracy and real-time inference. Through our work, we aim to assist the development of a mobile application for disease identification.