Department of Computer Science and Engineering, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh.
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu 501-1193, Gifu, Japan.
Sci Rep. 2024 Nov 22;14(1):28946. doi: 10.1038/s41598-024-78598-7.
The quality and uniformity of green coffee beans significantly influence the overall flavor and value of the product. In the coffee industry, automated flaws and bean-type identification offer numerous advantages. This study examines the effectiveness of multiple YOLO (You Only Look Once) models for identifying and classifying green coffee beans. Various YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and custom models, are compared with a focus on computational efficiency, accuracy, and speed. Utilizing a dataset of 4,032 training and 506 testing images encompassing diverse bean types, defects, and lighting conditions, we assessed the performance. The bounding boxes generated by our models accurately encompass coffee beans, with the background typically uniform and set to black. Our analysis reveals the superior performance of the custom-YOLOv8n model, which we meticulously customized for green coffee bean detection. This model achieved high precision, recall, f1-score, and mAP, demonstrating its potential for real-world implementation in coffee bean quality control systems. The customization process involved fine-tuning the model to focus on significant features relevant to green coffee bean detection and employing specific labeling strategies. Customization allows you to fine-tune the model to focus on important features relevant to green coffee bean detection. This sensitivity ensures that the model can effectively distinguish between different bean types and detect even subtle defects. This paper clarifies our primary objective of evaluating YOLO models' performance in identifying and categorizing green coffee beans, with potential implications for enhancing efficiency and consistency in the coffee industry. A succinct key sentence underscores the benefits of our research for readers seeking efficient YOLO model selection and implementation in agricultural systems.
咖啡豆的质量和均匀度对产品的整体风味和价值有重大影响。在咖啡行业中，自动化瑕疵和豆型识别有诸多优势。本研究考察了多个 YOLO（只看一次）模型在识别和分类绿咖啡豆方面的有效性。我们比较了各种 YOLO 变体，包括 YOLOv3、YOLOv4、YOLOv5、YOLOv7、YOLOv8 和自定义模型，重点关注计算效率、准确性和速度。我们利用一个包含 4032 个训练图像和 506 个测试图像的数据集，涵盖了各种豆型、缺陷和光照条件，评估了模型的性能。我们的模型生成的边界框准确地包含了咖啡豆，背景通常是均匀的，设置为黑色。我们的分析表明，自定义-YOLOv8n 模型表现出色，我们对其进行了精心定制，用于绿咖啡豆检测。该模型实现了高精度、高召回率、高 F1 分数和高 mAP，表明其在咖啡豆质量控制系统中的实际应用潜力。定制过程包括微调模型，使其专注于与绿咖啡豆检测相关的重要特征，并采用特定的标注策略。定制可以帮助你调整模型，使其专注于与绿咖啡豆检测相关的重要特征。这种敏感性确保模型能够有效地区分不同的豆型，并检测到甚至细微的缺陷。本文阐明了我们的主要目标，即评估 YOLO 模型在识别和分类绿咖啡豆方面的性能，这对提高咖啡行业的效率和一致性具有潜在意义。一个简洁的关键句强调了我们的研究对寻求高效 YOLO 模型选择和在农业系统中实施的读者的益处。
