A Light Weight CNN Algorithm for the Detection of Various Tomato Plant Diseases

Food security and agricultural output are seriously threatened by tomato plant diseases. Detecting these diseases early and accurately is essential for managing and mitigating their impact. In this study, we introduce a lightweight Convolutional Neural Network (Lw-CNN) algorithm specifically designed to identify a variety of tomato plant diseases. Our model is optimized to run efficiently on devices with limited computational resources, without sacrificing accuracy. The results of the proposed work are tested on publicly plant village dataset. The results show that Lw-CNN model is highly accurate, precise, and reliable in recognizing tomato plant diseases. This lightweight solution provides a practical tool for real-time disease monitoring in agricultural environments, helping farmers make timely and informed decisions to protect their crops.