• Sharmila M
• Natarajan M

Tomato plant leaf and fruit diseases are a significant concern for farmers and gardeners as they can cause substantial yield losses and affect crop quality. Detecting these diseases early is crucial for implementing timely control measures and minimizing the spread of infections. In recent years, machine learning (ML) and deep learning (DL) techniques have exposed great promise in automating the detection method, providing accurate and efficient disease diagnosis. This article presents a comprehensive review of the application of ML and DL techniques for tomato plant disease detection. The aim of this survey is to investigate the utilization and effectiveness of ML and DL techniques in the detection of tomato plant leaf and fruit diseases. The survey begins by providing an overview of the common diseases affecting tomato plants, highlighting the importance of timely detection and intervention. It then delves into the principles and methodologies of ML and DL, providing a foundation for understanding their applications in disease detection. The survey examines the existing ML and DL techniques available for plant disease detection. Furthermore, the DL approaches, particularly convolutional neural networks (CNNs) are exposed to significant performance in tomato plant disease detection. CNNs can automatically learn hierarchical features in raw image data, enabling accurate disease classification. This survey paper serves as a valuable resource for researchers, practitioners, and stakeholders in the agricultural domain, facilitating the adoption of advanced technologies for early and accurate tomato plant disease detection, leading to improved crop management practices and higher agricultural productivity.

Tomato diseases; Plant disease detection; Machine learning; Computer vision; Deep learning

"A COMPREHENSIVE SURVEY ON MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR TOMATO PLANT LEAF AND FRUIT DISEASE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.c870-c875, July-2023, Available :http://www.jetir.org/papers/JETIR2307296.pdf

"A COMPREHENSIVE SURVEY ON MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR TOMATO PLANT LEAF AND FRUIT DISEASE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppc870-c875, July-2023, Available at : http://www.jetir.org/papers/JETIR2307296.pdf