Enhancing Yam Quality Detection through Computer Vision in IoT and Robotics Applications
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".