Apple Fruit Disease Detection using Deep Learning

ABSTRACT:

The agricultural sector is significantly impacted by fruit diseases, particularly those affecting apples, which can lead to reduced yield and economic losses. To address this issue, the project “Apple Fruit Disease Detection using Deep Learning” presents an intelligent and automated solution for identifying diseases in apple fruits through advanced image classification techniques. Developed using Python for backend processing and HTML, CSS, and JavaScript for the frontend interface, the application is deployed on a lightweight Flask web framework to enable seamless user interaction.

The core of the system utilizes the Inception V3 deep learning architecture, known for its superior performance in image recognition tasks. The model is trained to classify apple fruit images into four categories: Blotch Apple (146 images), Normal Apple (130 images), Rot Apple (134 images), and Scab Apple (100 images).

After rigorous training and testing, the model achieved an impressive performance with an accuracy of 99.7%, precision of 1.000, recall of 0.997, and an F-measure of 0.997, demonstrating its robustness and reliability in real-world scenarios.

The need for this project arises from the growing challenges faced in the agricultural sector, particularly in fruit cultivation, where diseases can cause significant damage to crop yield, quality, and profitability. Apple, being a commercially valuable fruit crop, is highly vulnerable to various diseases such as blotch, rot, and scab. Manual identification of these diseases is often time-consuming, labor-intensive, and error-prone due to reliance on human expertise and environmental variability. This project addresses these issues by introducing an intelligent and automated system for disease detection.

This system offers a scalable and user-friendly platform for farmers, agricultural experts, and supply chain stakeholders to detect apple fruit diseases early and accurately, thereby supporting timely intervention and quality assurance.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Inception V3 Architecture.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 20 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 8 GB.

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

Python Projects

Apple Fruit Disease Detection using Deep Learning