Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Foods, 2022, 11, (21), pp. 3483
- Issue Date:
- 2022-11-02
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%.
Please use this identifier to cite or link to this item: