Enhancing Yam Quality Detection through Computer Vision in IoT and Robotics Applications

Abstract

Abstract This study introduces a comprehensive framework aimed at automating the process of detecting yam tuber quality attributes. This is achieved through the integration of Internet of Things (IoT) devices and robotic systems. The primary focus of the study is the development of specialized computer codes that extract relevant image features and categorize yam tubers into one of three classes: "Good," "Diseased," or "Insect Infected." By employing a variety of machine learning algorithms, including tree algorithms, support vector machines (SVMs), and k-nearest neighbors (KNN), the codes achieved an impressive accuracy of over 90% in effective classification. Furthermore, a robotic algorithm was designed utilizing an artificial neural network (ANN), which exhibited a 92.3% accuracy based on its confusion matrix analysis. The effectiveness and accuracy of the developed codes were substantiated through deployment testing. Although a few instances of misclassification were observed, the overall outcomes indicate significant potential for transforming yam quality assessment and contributing to the realm of precision agriculture. This study is in alignment with prior research endeavors within the field, highlighting the pivotal role of automated and precise quality assessment. The integration of IoT devices and robotic systems in agricultural practices presents exciting possibilities for data-driven decision-making and heightened productivity. By minimizing human intervention and providing real-time insights, the study approach has the potential to optimize yam quality assessment processes. Therefore, this study successfully demonstrates the practical application of IoT and robotic technologies for the purpose of yam quality detection, laying the groundwork for progress in the agricultural sector.