用视觉识别实时分选蟋蟀性别，提升养殖效率
Real-time Cricket Sorting By Sex
- 用YOLOv8纳米模型+树莓派实现快速识虫
- 实测分选准确率达86.8%，[email protected]达0.977
- 适合昆虫养殖厂部署，低成本可扩展
全球对可持续蛋白源的需求推动了食用昆虫的关注，家蟋蟀（Acheta domesticus）被视为工业养殖的理想物种。当前养殖多采用混合性别群体，缺乏自动化性别分选，而性别分选有助于定向育种、优化繁殖比例及营养调控。本文提出一种低成本、实时的家蟋蟀性别自动分选系统，结合计算机视觉与物理执行装置。系统基于树莓派5与官方AI摄像头，集成自研YOLOv8 nano目标检测模型，并配备舵机驱动分选臂。测试中模型[email protected]达0.977，实际群组实验整体分选准确率为86.8%。结果表明，轻量级深度学习模型可在资源受限设备上有效部署，为蟋蟀养殖提供高效可持续的解决方案。
原文摘要 · Abstract (English)
The global demand for sustainable protein sources is driving increasing interest in edible insects, with Acheta domesticus (house cricket) identified as one of the most suitable species for industrial production. Current farming practices typically rear crickets in mixed-sex populations without automated sex sorting, despite potential benefits such as selective breeding, optimized reproduction ratios, and nutritional differentiation. This work presents a low-cost, real-time system for automated sex-based sorting of Acheta domesticus, combining computer vision and physical actuation. The device integrates a Raspberry Pi 5 with the official Raspberry AI Camera and a custom YOLOv8 nano object detection model, together with a servo-actuated sorting arm. The model reached a mean Average Precision at IoU 0.5 ([email protected]) of 0.977 during testing, and real-world experiments with groups of crickets achieved an overall sorting accuracy of 86.8%. These results demonstrate the feasibility of deploying lightweight deep learning models on resource-constrained devices for insect farming applications, offering a practical solution to improve efficiency and sustainability in cricket production.
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