Open AccessSmart agriculture: real‐time classification of green coffee beans by using a convolutional neural network
Author(s) -
Huang NenFu,
Chou DongLin,
Lee ChiaAn,
Wu FengPing,
Chuang AnChi,
Chen YiHsien,
Tsai YinChun
Publication year - 2020
Publication title -
iet smart cities
Language(s) - English
Resource type - Journals
ISSN - 2631-7680
DOI - 10.1049/iet-smc.2020.0068
Subject(s) - convolutional neural network , agricultural engineering , agriculture , coffee bean , artificial intelligence , quality (philosophy) , green coffee , deep learning , computer science , machine learning , agricultural science , engineering , food science , geography , environmental science , philosophy , chemistry , archaeology , epistemology
Coffee is an important economic crop and one of the most popular beverages worldwide. The rise of speciality coffees has changed people's standards regarding coffee quality. However, green coffee beans are often mixed with impurities and unpleasant beans. Therefore, this study aimed to solve the problem of time‐consuming and labour‐intensive manual selection of coffee beans for speciality coffee products. The second objective of the authors’ study was to develop an automatic coffee bean picking system. They first used image processing and data augmentation technologies to deal with the data. They then used deep learning of the convolutional neural network to analyse the image information. Finally, they applied the training model to connect an IP camera for recognition. They successfully divided good and bad beans. The false‐positive rate was 0.1007, and the overall coffee bean recognition rate was 93%.