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All Journal Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Conference on Innovation and Application of Science and Technology (CIASTECH) Infotekmesin Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Rainstek : Jurnal Terapan Sains dan Teknologi Journal of Mechanical and Electrical Technology Jurnal Bina Komputer Jurnal INFOTEL
Irawan, Novta Danyel
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences

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A Novel Approach to Defect Detection in Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation and Model Optimization Ardian, Yusriel; Irawan, Novta Danyel; Sutoko, Sutoko; Astawa, I Nyoman Gede Arya
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian hyperparameter optimization to improve defect detectability and expedite the training period. During testing, the defined model demonstrated excellent efficiency in defect detection, with a 97.29% confidence level, which was higher than that of the modified VGG16 and Slim-CNN models. The goal of the second optimization was an improvement of the practical application of the model. In terms of the time it takes for a model to be trained, approximately 30% of the time was saved. These findings present a consistent and effective way for the mass production processes of coffee to have quality control procedures automated. The model's ability to detect defects in other agricultural items makes it attractive, thus serving as a practical example of how AI can impact effective management in the inspection processes. The research further enriches the study of deep learning applications in agriculture by demonstrating how to efficiently address specific defect detection problems through an optimized convolutional neural network model.
Co-Authors Agung Sulistyo Arianti Kusumawardhani Djoko Adi Susilo Dwiyanto, Felix Andika I D, Riski Nur I Nyoman Gede Arya Astawa Muhammad Athoillah Onny Setyawati Pradnyana, I Putu Bagus Arya Purnama, Ida Bagus Irawan Riski Nur Istiqomah Dinnullah Shafiq Nurdin Shafiq Nurdin Sutoko, Sutoko Wijono Yordano Dwi Yunan Bramasta Yunus E. P., Amak Yusriel Ardian