Zhang Hongzhou, Li Qingxu, Luo Zhenwei
College of Mechanical and Electrical Engineering, Tarim University, Alar, China.
Institute of Cotton Engineering, Anhui University of Finance & Economics, Bengbu, China.
Front Plant Sci. 2024 Aug 9;15:1418224. doi: 10.3389/fpls.2024.1418224. eCollection 2024.
High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.
优质棉籽对于棉花的成功种植至关重要。棉籽壳的完整性在促进棉花植株的发芽和生长方面起着关键作用。因此，在棉花种植过程之前清除破碎棉籽至关重要。遗憾的是，在这个关键阶段缺乏快速且经济高效的破碎棉籽检测方法。为了解决这个问题，本研究开发了一种双相机系统，用于获取多个棉籽的正面和背面图像。基于该系统，我们设计了在线检测棉籽破损所需的硬件、软件和控制系统。此外，为了提高棉籽破损检测的性能，我们通过合并MobileOne-block和GhostConv改进了YOLOV8m的主干和YOLO头，从而得到了Light-YOLO。Light-YOLO在检测棉籽破损方面实现了93.8%的精度、97.2%的召回率、98.9%的mAP50和96.1%的准确率，模型紧凑，大小为41.3MB。相比之下，YOLOV8m的指标为93.7%的精度、95.0%的召回率、99.0%的mAP50和95.2%的准确率，模型大小更大，为49.6MB。为了进一步验证在线检测设备和Light-YOLO的性能，本研究进行了在线验证实验，棉籽破损信息的检测准确率为86.7%。结果表明，与YOLOV8m相比，Light-YOLO具有卓越的检测性能和更快的速度，证实了本研究提出的在线检测技术的可行性。该技术为分选破碎棉籽提供了一种有效的方法。
