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title

Leaf Disease Prediction Using Classification in Machine Learning and GUI

Author(s):

Shalini.L

Keywords:

Leaf Disease, Machine Learning, Convolutional Neural Networks, Image Processing, Agricultural Management, GUI

Abstract

In agriculture, timely detection and classification of leaf diseases play a crucial role in ensuring crop health and yield optimization. With the advent of machine learning (ML) techniques, automated systems have emerged as effective tools for disease identification, aiding farmers in making informed decisions regarding crop management. This project presents a novel approach for leaf disease classification using ML algorithms, coupled with the prediction of suitable fertilizers and the season during which the disease is likely to affect the crop. The proposed system leverages image processing techniques to extract features from leaf images, which are then used as input for ML models. Various classifiers such as Convolutional Neural Networks (CNN) are trained on a dataset comprising images of healthy and diseased leaves to classify different types of leaf diseases accurately. Overall, the integration of ML-based disease classification, fertilizer recommendation, and season prediction offers a comprehensive solution for addressing leaf diseases in crops, empowering farmers with actionable insights for efficient crop management. Finally, the output is obtained through a Graphical User Interface (GUI), which allows users to interact with the system through graphical icons.

Other Details

Paper ID: IJSARTV
Published in: Volume : 10, Issue : 8
Publication Date: 8/6/2024

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