Multi-Task Learning for Tomato Crop Disease Detection and Severity Estimation using CNN Framework

The cultivation of tomatoes holds immense significance in the agricultural landscape of the Indian region, also it plays a substantial role in economic and nutritional contributions. Nonetheless, the widespread occurrence of diseases poses a notable risk to both the quantity and quality of tomato yields. This study endeavors to create a real-time application employing a deep learning mechanism in conjunction with a multi-task method. In this study, the process utilizes convolutional neural networks (CNN) and VGG16 architecture. The determination of disease severity and the stage of the disease is conducted through multi-task learning methods integrated with CNN, providing a comprehensive approach to address the challenges associated with disease threats in tomato cultivation.