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

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.

Plant phenotyping relevance

ヤム塊茎の品質・病害・虫害状態を画像特徴から分類するコンピュータビジョン手法を開発し、機械学習およびロボット実装で精度検証しており、植物状態の取得・判定が中心である。

Code and data availability

The paper describes 300 self-acquired yam tuber images, MATLAB feature-extraction codes, and a supplementary file (Table S1 with extracted features), but no public deposit or authors' URL for the dataset, images, or code is provided. The supplementary file is only referenced as a downloadable docx on the preprint, and

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