Smart agriculture: real‐time classification of green coffee beans by using a convolutional neural network. Issue 4 (13th October 2020)

• Record Type: Journal Article
• Authors: Huang, Nen‐Fu; Chou, Dong‐Lin; Lee, Chia‐An; Wu, Feng‐Ping; Chuang, An‐Chi; Chen, Yi‐Hsien; Tsai, Yin‐Chun
• Is Part Of: IET smart cities. Volume 2:Issue 4(2020)
• Page Start: 167
• Page End: 172
• Publication Date: 2020-10-13
• Subjects: quality control -- image classification -- learning (artificial intelligence) -- beverages -- crops -- feature extraction -- agriculture -- convolutional neural nets; real‐time classification -- green coffee beans -- convolutional neural network -- economic crop -- coffee quality -- unpleasant beans -- labour‐intensive manual selection -- speciality coffee products -- automatic coffee bean picking system -- coffee bean recognition rate -- smart agriculture -- image processing -- data augmentation technologies -- deep learning; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/iet-smc.2020.0068
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract : 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%.