Tomato Leaf Disease Detection Using a Convolutional Neural Network

Tomatoes are one of the most widely farmed and consumed vegetables in the world, and they are essential to the agricultural industry. Tomato plants are unfortunately prone to a number of illnesses, which can result in decreased production and quality and substantial financial losses. Grey leaf spot is one of the most prevalent diseases affecting tomato plants. It damages and destroys the leaves, which eventually prevents the plant from bearing fruit. Contact, invasion, latency, and onset are the four stages of the infection that the pathogen that causes grey leaf spot in tomato plants goes through. Early illness detection can provide effective management strategies and potentially avert widespread pandemics. Solanum lycopersicum, often known as the South Indian tomato, is a widely used vegetable in cooking. The tomato plant is widely cultivated and regarded as a significant agricultural commodity due to its ability to adapt to a range of environmental conditions and its abundance in potassium, vitamins C and K, and other minerals. However, growing tomatoes is not without its difficulties. Leaf diseases, which can reduce yields by as much as 60%, are one major challenge. The likelihood that conventional detection methods won't be able to keep up with the quick trends in plant diseases underscores the urgent need for creative alternatives. NNs provide a new technique for identifying tomato leaf disease. An inventive technique that utilizes and accurately diagnoses tomato leaf diseases is presented in this study. neural networks with convolutions.