期刊
FOODS
卷 11, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/foods11213483
关键词
fruit defect detection; machine learning; deep learning; convolutional neural network; mango
资金
- University of Technology Sydney Internal Fund
The article highlights the importance of machine learning techniques in agricultural applications, specifically in developing a computer-assisted system for mango quality grading and defect detection. Efficient classification results were achieved using deep learning methods, particularly CNN.
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%.
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