Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 14 August 2024 (09:21:16 CEST)
How to cite:
Nguembang Fadja, A.; Che, S. R.; Atemkemg, M. Intelligent Vision System for Identifying Defects on African Plum Surfaces. Preprints2024, 2024080992. https://doi.org/10.20944/preprints202408.0992.v1
Nguembang Fadja, A.; Che, S. R.; Atemkemg, M. Intelligent Vision System for Identifying Defects on African Plum Surfaces. Preprints 2024, 2024080992. https://doi.org/10.20944/preprints202408.0992.v1
Nguembang Fadja, A.; Che, S. R.; Atemkemg, M. Intelligent Vision System for Identifying Defects on African Plum Surfaces. Preprints2024, 2024080992. https://doi.org/10.20944/preprints202408.0992.v1
APA Style
Nguembang Fadja, A., Che, S. R., & Atemkemg, M. (2024). Intelligent Vision System for Identifying Defects on African Plum Surfaces. Preprints. https://doi.org/10.20944/preprints202408.0992.v1
Chicago/Turabian Style
Nguembang Fadja, A., Sain Rigobert Che and Marcellin Atemkemg. 2024 "Intelligent Vision System for Identifying Defects on African Plum Surfaces" Preprints. https://doi.org/10.20944/preprints202408.0992.v1
Abstract
Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2,892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG16, Detectron-121, MobileNet and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10-30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
