Authors

Abstract

In order to accurately and quickly achieve wheat grain detection and counting, and to efficiently evaluate wheat quality and yield, a lightweight YOLOv8 algorithm is proposed to automatically count wheat grains in different scenarios. Firstly, wheat grain images are collected under three scenarios: no adhesion, slight adhesion, and severe adhesion, to create a dataset. Then, the neck network of YOLOv8 is modified to a bidirectional weighted fusion BiFPN to establish the wheat grain detection model. Finally, the results of wheat grain counting are statistically analyzed. Experimental results show that after lightweight improvement of YOLOv8 with BiFPN, the mAP (mean Average Precision) value of wheat grain detection is 94.7%, with a reduction of 12.3% in GFLOPs. The improved YOLOv8 model now requires only 9.34ms for inference and occupies just 4.0MB of memory. Compared with other models, the proposed model in this paper performs the best in terms detection accuracy and speed comprehensively, better meeting the real-time counting requirements of wheat grains.

Abstract in Chinese

为了准确、快速实现小麦籽粒目标检测和计数，更为高效的对小麦品质和产量进行评估，提出一种轻量化的YOLOv8算法实现不同场景下小麦籽粒自动计数。首先采集无粘连、轻微粘连、重度粘连3种场景下的小麦籽粒图像创建数据集。然后将YOLOv8的neck网络改成双向加权融合的 BiFPN，建立小麦籽粒检测模型。最后，对小麦籽粒计数结果进行统计。试验结果表明，YOLOv8进行BiFPN的轻量化改进后，小麦籽粒检测mAP值为94.7%，GFLOPs减少了12.3%。改进后的YOLOv8模型推理时间仅需9.34ms，内存占用仅为 4.0MB。将改进后的YOLOv8模型与其他模型相比，本文提出的模型在检测精度和速度综合方面性能最优，更能满足小麦籽粒实时计数要求。