Paddy Disease Classification in Tamil Nadu Crops utilizing Image Analysis Using Deep Learning Techniques

Abstract

Paddy is one of the staple food crops in Tamil Nadu, which ensures enough food availability to several million farmers and provides socio-economic prosperity; however, it has been found immensely susceptible to various types of diseases like bacterial leaf blight, blast, and brown spot which may cause a severe reduction in yield and quality. Early detection and accurate diagnosis of these diseases are important for a timely intervention and management, since the traditional ways of identifying these diseases are very laborious, time-consuming, and prone to human errors.

This project puts forward a novel classification of paddy diseases using image analysis and deep learning techniques based on state-of-the-art advancements in artificial intelligence to develop an efficient, effective, and scalable solution. The major aim of the present project is to design a strong automatic classification system for diseases in the paddy through leaf images. This will comprise collecting comprehensive data sets of paddy leaf images affected by various diseases, preprocessing these images to enhance their quality, and designing convolutional neural networks able to classify these diseases.

With this trained model, farmers will be able to conduct real-time diagnosis of paddy diseases using deep learning and image analysis of the affected area. The farmers will receive all the details of the disease and some suitable treatment options; therefore, they will make quick and correct decisions. This project will mean a lot to agricultural technology; that simply means deep learning can be harnessed in the light of imperative issues in agriculture, such as managing diseases. This again shows how AI-driven technology does not necessarily require a web-based application to support better agricultural practices