首页|Real-time defect inspection of green coffee beans using NIR snapshot hyperspectral imaging
Real-time defect inspection of green coffee beans using NIR snapshot hyperspectral imaging
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NSTL
Elsevier
? 2022 Elsevier B.V.Coffee beans are important agricultural commodities traded in the international market. Screening for defective beans is an important step before roasting. The main types of defective beans include black, fermented, moldy, insect damaged, shell, and broken. Insect-damaged beans are the most common type of defective beans. Previously, coffee beans were sorted manually, which was extremely labor intensive and prone to fatigue-induced errors, resulting in inconsistent quality. This study combines a near-infrared snapshot hyperspectral sensor and deep learning to create a multimodal real-time coffee-bean defect inspection algorithm (RT-CBDIA) for sorting defective green coffee beans. Furthermore, three convolutional neural networks (CNN) were designed to achieve real-time inspection, i.e., lean 2D-CNN, 3D-CNN, and 2D–3D-merged CNN. Subsequently, principal component analysis was used to select important bands. Our experimental results achieved an overall accuracy of 98.6% using 1026 green coffee-bean samples. Furthermore, the RT-CBDIA achieved a Kappa value of 97.2% and real-time sorting speeds. These achievements are considerably beneficial for subsequent applications and the commercialization of smart agriculture. Our main objective is to commercialize the proposed RT-CBDIA algorithm by combining it with a robot to create a comprehensive yet affordable coffee-bean real-time inspection system. It can be used to achieve real-time and noninvasive inspections while reducing labor costs. In the future, our real-time inspection system can also be applied to other crops to ultimately advance smart agriculture.
NSTL
Elsevier
