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深度学习的应用用于果子缺陷识别Psidium guajava L

Kuo-Dung Chiou^1,2, Yen-Xue Chen³, Po-Sung Chen^4,5

¹Fengshan Tropical Horticultural Experiment Branch, Taiwan Agricultural Reserch Institute, Ministry of Agriculture, Kaohsiung, 830014, Taiwan.

Scientific reports

|February 20, 2025

概括

这项研究开发了一种人工智能模型,可以自动检测Psidium guajava L. (瓜) 的水果缺陷. YOLO v4 模型实现了高精度,使得这个重要的热带水果能够实时进行质量控制.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
食品质量控制食品质量控制

背景情况:

瓜 (guava) 是一个重要的热带和亚热带水果,在台湾全年生产,需要质量标准化.
外观缺陷对收获瓜的销售能力和标准化有重大影响.

研究的目的:

开发和评估用于检测和分类瓜子外观缺陷的自动化系统.
提高瓜收获和分类质量控制的效率和准确性.

主要方法:

使用YOLO v4预训练的网络架构进行缺陷检测.
收集了189个Psidium guajava L.水果的1701张图像,来自各种农场,将缺陷分为13个类别.
使用诸如假阳性率,假阴性率和准确性等指标评估模型性能.

主要成果:

YOLO v4 模型在缺陷检测方面实现了 88.15% 的整体准确性.
对于一般缺陷的假阳性 (6.62%) 和假阴性 (5.03%) 率较低.
在特定的真菌病 (Colletotrichum gloeosporoides,Pestalotiopsis psidii,Phyllosticta psidiicola) 中,虚假阳性和虚假阴性率达到了不到9%.
展示了实时适用性,最小可检测缺陷大小为13×14像素,处理速度为12 FPS.

结论:

YOLO v4模型对于自动检测Psidium guajava L.中的外观缺陷非常有效.
该系统的性能支持其在实时收获和分级过程中的实施,以加强质量控制.
这种由人工智能驱动的方法为标准化瓜质量提供了强大的解决方案.

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