Color-based Classification of Dried Cocoa Beans from Various Origins of Indonesia by Image Analysis Using AlexNet and ResNet Architecture-Convolutional Neural Networks
Wahyu Kristianingsih | Bambang Dwi Argo | Misnawi Jati | Noor Ariefandie Febrianto | Yusuf Hendrawan | Mochamad Bagus Hermanto | Bagus Rahmatullah
Cocoa plant is widely cultivated in Indonesia and spread across various regions. Diversity in geographical conditions has been known to significantly affect the quality of cocoa beans. Practically, cocoa beans are often mixed without considering the variation in the quality and its origin. This resulted in reduced global quality and product inconsistency. Improved recognition and classification methods are needed to solve those problems. Non-destructive classification methods can be used to provide a more efficient classification process. The use of artificial intelligence with computer-based deep learning methods was used in this study. Beans samples of various origins (Aceh, Bali, Banten, Yogyakarta, East Kalimantan, West Sulawesi, and West Sumatera) were evaluated. From the collected samples, 9100 images were then taken for data processing. Data preprocessing included denoising of the background image, cropping, resizing and changing the storage extension through the training-validation stage and the testing process. AlexNet and ResNet architectures on a Convolutional Neural Network were used for classification. The results showed that the average accuracy of cocoa image classification based on color identification by computer machines using Alexnet and ResNet was high (99.91% and 99.99%, respectively). This method can be applied to provide more efficient color-based cocoa bean classification for industrial purposes.
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