Enhancing Yam Quality Detection through Computer Vision in IoT and Robotics Applications
John Audu,
Adeyemi Adegbenjo,
Emmanuel Ajisegiri
et al.
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, … Show more
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