Identification of plant Syndrome using IPT

Madan Mohan M; Malathi V; VinothiniI N

Author:

Madan Mohan M, Malathi V, VinothiniI N

Published in

Journal of Science Technology and Research

( Volume , Issue )

Page No: 60-69

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Abstract

Agricultural productivity is something on which Indian economy highly depends. This is the one of the reasons that disease detection in plants plays a vital role in agriculture field, as having disease in plants are unavoidable. If proper care is not taken in this area, then it causes serious effects on plants and due to which the overall agriculture yield will be affected. For instance, a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself if detected properly by identifying the symptoms of diseases can result in increased productivity. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers diseases classification techniques that can be used for plant leaf disease detection. Image segmentation is one of the method which will segment the raw images in to two or more clusters and the programmed algorithm will work fine in analyzing these clusters for disease classification and prediction of type of disease that a plant leaf gets affected

Keywords

Image processing, Detection, Identification of plant leaf diseases, Convolutional neural network

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ABSTRACT:

Automatic Detection of Plant Leaf Disease using Image Processing plays a critical role in improving agricultural productivity, which is central to the Indian economy. Plant diseases, though unavoidable, can severely reduce crop yield if not addressed early. Traditional manual monitoring over large farms is time-consuming, labor-intensive, and often inaccessible to small farmers. This project proposes an automated method for early plant disease detection using image segmentation and classification techniques. The algorithm processes plant leaf images, segments them into clusters, and analyzes features to classify the type of disease accurately. For example, early detection of hazardous diseases like Little Leaf Disease in pine trees can significantly reduce crop loss. By using image processing and machine learning, this solution enables farmers to detect symptoms early and take timely action. The automated system minimizes the need for human monitoring and lowers the cost of disease diagnosis, making it scalable, efficient, and suitable for widespread use in agriculture.

INTRODUCTION:

Automatic Detection of Plant Leaf Disease using Image Processing is vital for modern agriculture, especially in India where farming heavily supports the economy. Early detection of plant diseases helps prevent significant yield loss and lowers the cost of intervention. Diseases like Little Leaf Disease in pine trees demonstrate how devastating delayed detection can be. Affected trees experience stunted growth and often die within a few years, particularly in regions like Alabama and Georgia in the southern U.S.Manually monitoring crops across large farms is costly and requires expert knowledge, which is not always accessible to small-scale farmers. As a result, many fail to detect plant diseases in time. Image processing offers a solution by allowing automated detection through leaf image analysis. An algorithm can be trained on samples of healthy and diseased leaves to recognize patterns and classify plant health. With this approach, farmers can quickly and affordably identify issues and take preventive action.

Identification of plant Syndrome using IPT