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Related Experiment Video

Updated: Aug 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.

R Nithya¹, B Santhi¹, R Manikandan¹

¹School of Computing, SASTRA Deemed University, Thanjavur 613401, India.

Foods (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

Automated mango quality grading using machine learning achieves 98% accuracy. This computer vision system, employing convolution neural networks (CNNs), offers a faster, more consistent alternative to manual fruit inspection for the export market.

Keywords:

convolutional neural network deep learning fruit defect detection machine learning mango

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Area of Science:

Agricultural Technology
Computer Vision
Machine Learning

Background:

Manual fruit grading is subjective and inefficient, impacting export quality.
Automated systems are crucial for improving agricultural productivity and economic growth.
Defect detection on fruit surfaces is vital for the export market, especially for popular fruits like mangoes.

Purpose of the Study:

To develop a computer-assisted grading system for defect detection in mangoes.
To implement a machine learning approach for automated mango quality classification.
To enhance the efficiency and consistency of mango grading for the export market.

Main Methods:

A computer-vision system utilizing convolution neural networks (CNNs) was developed.
The system was trained and tested using a publicly available mango image database.
Deep learning techniques were employed for digital image classification and defect detection.

Main Results:

The proposed CNN-based system achieved a high accuracy of 98% in classifying mango quality.
Experimental results demonstrated the effectiveness of the automated system.
The system successfully identified defects on mango surfaces.

Conclusions:

The developed computer-vision system provides an accurate and efficient solution for mango quality grading.
Machine learning, specifically CNNs, offers a powerful tool for automating agricultural quality assessment.
This automated approach can significantly improve the consistency and speed of fruit inspection for export markets.