用低成本视觉系统和气动分拣，自动识别六类绿扁豆。
Low-Cost Machine Vision System for Sorting Green Lentils (Lens Culinaris) Based on Pneumatic Ejection and Deep Learning
- 两阶段YOLOv8模型检测并分类绿扁豆
- 在59毫米/秒速度下准确率87.2%
- 适合农业自动化与小规模设备开发
本文提出一种基于计算机视觉与气动分拣的动态谷物分类系统，用于绿扁豆（Lens Culinaris）的智能分选。系统采用双阶段YOLOv8架构：第一阶段检测传送带上的颗粒位置，第二阶段将其分类为六类——优质、发黄、破碎、去皮、斑点及废品。该方案部署于低成本模块化硬件平台，通过基于Arduino的控制系统实现实时协同。系统在59毫米/秒的传送速度下实现87.2%的分离准确率，虽处理速率仅8克/分钟，但仍验证了机器视觉在谷物分选中的可行性，并为后续优化提供可扩展框架。
原文摘要 · Abstract (English)
This paper presents the design, development, and evaluation of a dynamic grain classification system for green lentils (Lens Culinaris), which leverages computer vision and pneumatic ejection. The system integrates a YOLOv8-based detection model that identifies and locates grains on a conveyor belt, together with a second YOLOv8-based classification model that categorises grains into six classes: Good, Yellow, Broken, Peeled, Dotted, and Reject. This two-stage YOLOv8 pipeline enables accurate, real-time, multi-class categorisation of lentils, implemented on a low-cost, modular hardware platform. The pneumatic ejection mechanism separates defective grains, while an Arduino-based control system coordinates real-time interaction between the vision system and mechanical components. The system operates effectively at a conveyor speed of 59 mm/s, achieving a grain separation accuracy of 87.2%. Despite a limited processing rate of 8 grams per minute, the prototype demonstrates the potential of machine vision for grain sorting and provides a modular foundation for future enhancements.
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