Identification of arabica coffee post-harvest processing using a convolutional neural network

Abstract

Indonesia's economy is greatly boosted by coffee, one of its flagship commodities. The post-harvest processing of coffee involves various processes, and the different methods have a crucial connection to the subsequent stages. Digital image analysis using Convolutional Neural Network (CNN) methods can be utilized to improve the identification of coffee beans. This study uses CNN with the ResNet-18 and MobileNetV2 architectures for image analysis. The research results show that the MobileNetV2 architecture produces the best accuracy of 98.89% at a data proportion of 70:20:10, and the ResNet-18 architecture produces the best accuracy of 99.56% at a data proportion of 50:25:25. This shows that both of them can handle differences in data proportions well in identifying the post-harvest process of Arabica coffee. The choice between the two can be considered based on available computational resources, desired model weight size, and relevant data proportion requirements for the desired application.