Chiou Kuo-Dung, Chen Yen-Xue, Chen Po-Sung, Jou Ying-Tzy, Tsai Shang-Han, Chang Chia-Ying
Fengshan Tropical Horticultural Experiment Branch, Taiwan Agricultural Reserch Institute, Ministry of Agriculture, Kaohsiung, 830014, Taiwan.
Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung, 91201, Taiwan.
Sci Rep. 2025 Feb 20;15(1):6145. doi: 10.1038/s41598-025-88936-y.
Psidium guajava L. is an important tropical and subtropical fruit. Due to its geographical location and suitable climate, Taiwan produces Psidium guajava L. all year round. Quality standardization is therefore a crucial issue. The primary objective was to detect appearance defects on harvested fruits. We divided the defects into thirteen classes, including damage from pests, diseases, and humans. We obtained 189 Psidium guajava L. fruits from different farms and collected 1701 images as samples. The YOLO v4 pretrained network architecture achieved excellent performance in defect detection, including a false positive rate of 6.62%, a false negative rate of 5.03%, and accuracy of 88.15%. Moreover, in the detection of Colletotrichum gloeosporoides, Pestalotiopsis psidii, and Phyllosticta psidiicola, the false positive and false negative detection rates were less than 9%. The applicability of the model in real-time harvesting and grading operations was demonstrated by a minimum detectable defect size of 13 × 14 pixels and computation speed of 12 FPS demonstrated.
番石榴是一种重要的热带和亚热带水果。由于其地理位置和适宜的气候，台湾全年都有番石榴产出。因此，品质标准化是一个关键问题。主要目标是检测收获果实上的外观缺陷。我们将缺陷分为十三类，包括虫害、病害和人为造成的损伤。我们从不同农场获取了189个番石榴果实，并收集了1701张图像作为样本。预训练的YOLO v4网络架构在缺陷检测方面表现出色，包括误报率为6.62%，漏报率为5.03%，准确率为88.15%。此外，在检测炭疽病菌、番石榴拟盘多毛孢和番石榴叶点霉时，误报和漏报检测率均小于9%。最小可检测缺陷尺寸为13×14像素以及12帧每秒的计算速度证明了该模型在实时收获和分级操作中的适用性。
