Rice and Other Crops Leaf Disease Classification

Project Code :TCMAPY1000

Objective

The goal of Rice and Other Crops Leaf Disease Classification is to provide an accurate and efficient automated system for diagnosing and classifying illnesses in the leaves of rice and other crops. By assisting farmers and other agricultural experts in early disease diagnosis, this seeks to facilitate focused intervention techniques. The system can distinguish between healthy and sick leaves as well as correctly categories the particular disease kind by utilizing cutting-edge machine learning algorithms and image processing. With the help of this technology, early disease diagnosis may be encouraged, which is essential for averting extensive crop damage, increasing production, and reducing financial losses. By giving farmers a dependable tool to accurately identify and manage leaf diseases, promoting sustainable crop production, the ultimate goal is to increase agricultural output and food security.

Abstract

Agriculture is essential for the economy and plant leaf disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract leaf color, shape, or texture data, thus aiding the detection of infections. Crop is one of the major cultivation in India which is affected by various diseases at various stages of its cultivation. It is very difficult for the farmers to manually identify these diseases accurately with their limited knowledge. Recent developments in Deep Learning show that Automatic Image Recognition systems using Convolutional Neural Network (CNN) with Transfer Learning (TL) models can be very beneficial in such problems. Since rice and tomato leaf disease image dataset is not easily available, we have created our own dataset which is small in size hence we have used Transfer Learning to develop our deep learning model. The proposed CNN architecture is based on MobileNet and is trained and tested on the dataset collected from rice fields and the internet.

KEYWORDS: Convolutional Neural Network, Deep Learning, crop diseases, Transfer Learning.

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