Abstract :

Fruit Diseases Significantly Affect Agricultural Productivity And Quality, Making Early And Accurate Detection Essential. This Project Presents An Intelligent Fruit Disease Detection System That Uses Image Processing, Machine Learning Techniques And A Convolutional Neutral Network(CNN) To Automatically Identify Diseases From Fruit Images. The System Allows Users To Upload An Image Of A Fruit, After Which Preprocessing Techniques Such As Resizing, Noise Removal, And Segmentation Are Applied To Extract Meaningful Features. A Trained Classification Model, The CNN Model Automatically Extracts Important Image Features Including Color, Texture, And Pattern Variations, Eliminating The Need For Manual Feature Engineering. Based On These Extracted Features, The Trained CNN Classifies The Fruit As Healthy Or Diseased Then Predicts The Type Of Disease Along With A Confidence Score. In Addition To Disease Identification, The System Analyzes The Infected Region To Estimate The Severity Level Based On The Proportion Of Affected Area. To Improve Reliability And User Trust, The System Also Displays Multiple Visual Variations Of The Predicted Disease, Enabling Users To Compare Symptoms. Furthermore, It Provides Detailed Descriptions And Practical Treatment Suggestions, Including Preventive And Corrective Measures. This Solution Aims To Assist Farmers And Agricultural Stakeholders By Offering A Fast, Cost-effective, And User-friendly Tool For Early Disease Detection, Severity Assessment, And Actionable Guidance To Reduce Crop Loss And Improve Productivity. Keywords: Fruit Disease Detection, Convolutional Neural Network (CNN), Image Processing, Deep Learning, Feature Extraction, Computer Vision, Agricultural Automation, Smart Farming, Plant Pathology Detection, Image Classification.

Published:

07-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

205-211

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite