Published Paper Details:

: Plant diseases are a common occurrence in the agricultural field, impacting crop productivity. The contribution of the economy to agriculture plays a significant role in enabling effective disease detection. The surveillance of large and diverse crop fields has increased the importance of plant disease detection. Farmers face challenges when transitioning between different disease management approaches. To ensure proper control measures and maintain plant health, timely detection of tomato leaf diseases is crucial. Mechanized techniques and methodologies offer efficient and constructive means of disease detection, reducing the labor-intensive task of surveillance in large-scale cultivation. Early detection of disease symptoms on plant leaves allows for prompt action. This review explores various algorithms used for image segmentation and automated classification in disease detection. It also encompasses different disease classification methods employed in plant disease detection. By employing deep learning technology, the disadvantages of artificial selection of disease spot features can be avoided, making disease feature extraction more objective and enhancing research efficiency and technological advancements. This review provides insights into the recent progress made in deep learning-based crop leaf disease identification, highlighting current trends, challenges, and the integration of advanced imaging techniques. It aims to serve as a valuable resource for researchers studying plant disease and insect pest detection, while also addressing existing challenges and unresolved issues