Fruit Quality Detection using Deep Learning for Rotten and Fresh Fruits Classification

IEEE BASE PAPER TITLE:

Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits

IEEE BASE PAPER ABSTRACT:

In the realm of agriculture, leveraging image processing has become pivotal for robust image analysis, especially in detecting fruit diseases. However, existing techniques in this domain often limit inputs to fixed sizes without reshaping images before neural network (NN) input, complicating disease detection and compromising image resolution, thereby escalating postharvest losses. To address this, an innovative approach has been developed a unique enhanced convolutional NN (CNN) employing spatial pyramid pooling (SPP) and adaptive momentum BP that integrates the best finite impulse response (FIR) filter for preprocessing. This method aims to reorganize the fruit detection process while maintaining high image resolution. The CNN, with its SPP, utilizes convolutional layers to extract diverse features encompassing color, shape, texture, and surface attributes crucial for accurate disease detection. Furthermore, efficient fruit grading is essential to combat issues such as poor product quality, slow grading speeds, and accuracy concerns, all contributing to postharvest losses. In response, a novel integrated stacked sparse denoising autoencoder–support vector machine (SSDAE-SVM) approach, coupled with dropout mechanisms, has been proposed to streamline fruit grading and mitigate postharvest losses. The strategic use of dropout layers mitigates overfitting and information loss during feature extraction, while the SVM classifier, serving as the output layer, ensures accurate fruit grading, thereby curbing postharvest losses. Consequently, this proposed method not only simplifies disease detection and grading processes but also enhances quality, accuracy, reliability, and speed. The model’s performance surpasses previous disease prediction models, exhibiting an impressive accuracy of 97.25%, a minimal prediction error of 0.15, a high specificity of 95.62%, an F1-score of 98.81%, and a remarkable recall rate of 98.98%.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

InceptionResNetV2 Architecture, MobileNetV2 Architecture.

OUR PROPOSED ABSTRACT:

Fruit quality assessment is a critical aspect of agricultural and food industries to ensure consumer satisfaction and reduce wastage. This project, Fruit Quality Detection using Deep Learning for Rotten and Fresh Fruits Classification, presents a web-based system that classifies fruits into three quality categories: Good Quality, Bad Quality, and Mixed Quality, across six fruit types: Apple, Banana, Guava, Lime, Orange, and Pomegranate.

The system is developed using Python for backend processing, with HTML, CSS, and JavaScript for the frontend interface, and Flask as the web framework. Two state-of-the-art deep learning models, InceptionResNetV2 and MobileNetV2, are employed to classify the images. The InceptionResNetV2 model achieved a training accuracy of 94.00% and a validation accuracy of 94.00%. In contrast, MobileNetV2 achieved superior performance with a training accuracy of 97.00% and a validation accuracy of 98.00%.

The dataset comprises 19,526 high-quality images, divided into three sub-categories: Good Quality (11,664 images), Bad Quality (6,788 images), and Mixed Quality (1,074 images). Each sub-category contains images of the six fruit types in a processed format.

The system demonstrates efficient and accurate classification, providing a robust tool for automated fruit quality detection, applicable in supply chain management and quality control in agriculture and retail sectors.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 8 GB.

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.12.0.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Himanshu B. Patel; Nitin J. Patil, “Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits”, in IEEE Sensors Journal, vol. 24, no. 5, pp. 6719-6732, March, 2024.

Python IEEE Projects

Fruit Quality Detection using Deep Learning for Rotten and Fresh Fruits Classification