An Optimized Deep Learning Model for the Detection and Classification of Tomato Plant Leaf Diseases Using Self-Developed CNN Model

Abstract

The research direction we propose for this paper is a first, optimizing deep learning to improve tomato plant leaf disease diagnosis according to the system developed in-house. Exploring agricultural technology and how specific aspects of it can be used at home requires looking closely at ground truths regarding such things as processing power improvements or model structure refinements that were made during program development by our own convolutional With such meticulous optimizing, we achieve a rational compromise between speed and accuracy which makes the model eminently usable in practice. The research highlights the transformative power of deep learning methods in terms of addressing tomato plant health issues, and represents a path-breaking model for future precision agriculture development.