access icon openaccess Smart agriculture: real-time classification of green coffee beans by using a convolutional neural network

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%.

Inspec keywords: image classification; feature extraction; quality control; beverages; convolutional neural nets; learning (artificial intelligence); agriculture; crops

Other keywords: convolutional neural network; speciality coffee products; coffee quality; real-time classification; green coffee beans; image processing; labour-intensive manual selection; automatic coffee bean picking system; unpleasant beans; smart agriculture; economic crop; data augmentation technologies; deep learning; coffee bean recognition rate

Subjects: Agriculture, forestry and fisheries computing; Inspection and quality control; Beverage industry; Image recognition; Computer vision and image processing techniques; Products and commodities; Agriculture; Neural computing techniques

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