YOLOv8n-LF model in detecting the impurity and broken rates of machine-harvested wheat

doi:10.14088/j.cnki.issn0439-8114.2025.10.031

Abstract

Abstract: To address the need for real-time detection of wheat impurity and broken rates during combine harvesting, a detection model (YOLOv8n-LF) was proposed based on an image segmentation method to achieve automatic assessment of wheat impurity and broken rates. First, the LSKA attention mechanism was introduced into the SPPF module to enhance the model’s multi-scale feature extraction capability. Second, the focal adjustment mechanism of Focal Loss was employed to optimize the CIoU loss function, focusing on different regression samples and improving the model’s detection performance. On a self-constructed dataset, the YOLOv8n-LF model maintained lightweight characteristics while demonstrating good segmentation performance, facilitating its deployment on edge devices. It can provide effective technical support for the automatic detection of wheat impurity and broken rates, thereby promoting the advancement of agricultural machinery intelligence.

Key words: YOLOv8n-LF model, machine-harvested wheat, impurity rate, broken rate, detection

CLC Number:

ZHOU Hong, MENG Xiao-yan, FANG Wei-zhou, DING Xiao-chen. YOLOv8n-LF model in detecting the impurity and broken rates of machine-harvested wheat[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(10): 201-206.

References

[1] 樊杰杰,邱春霞,樊意广,等.基于连续小波变换和机器学习的小麦产量预测[J].光谱学与光谱分析,2024,44(10):2890-2899.
[2] 张昆,袁博涵,崔静莹,等.基于计算机视觉的茶叶嫩芽识别方法研究进展[J].山东农业科学,2024,56(5):163-170.
[3] 欧利国,蓝振峰,刘必林,等.基于计算机视觉的鱼类形态轮廓特征自动提取[J].水产学报,2024,48(12):63-73.
[4] 周腊珍,陈红池,李秋霞,等.基于Transformer深度学习模型在医学图像分割中的研究进展[J].中国生物医学工程学报,2024, 43(4):467-476.
[5] ALKHALDI N A, ALABDULATHIM R E.Optimizing glaucoma diagnosis with deep learning-based segmentation and classification of retinal images[J]. Applied sciences, 2024, 14(17): 7795.
[6] 张帅. 基于多阈值分割的流动谷物含杂率破碎率监测方法及装置[D].江苏镇江:江苏大学,2021.
[7] 马志艳,张徐康,杨光友.基于改进Mask R-CNN的水稻茎秆杂质分割方法研究[J].中国农机化学报,2021,42(6):145-150.
[8] 金诚谦,刘士坤,陈满,等.采用改进U-Net网络的机收大豆质量在线检测[J].农业工程学报,2022,38(16):70-80.
[9] 陈满,金诚谦,莫恭武,等.基于改进U-Net模型的小麦收获含杂率在线检测方法[J].农业机械学报,2023,54(2):73-82.
[10] CHEN M, JIN C Q, NI Y L, et al.Online detection system for wheat machine harvesting impurity rate based on DeepLabV3+[J]. Sensors, 2022, 22(19): 7627.
[11] WU Z P, CHEN J, MA Z, et al.Development of a lightweight online detection system for impurity content and broken rate in rice for combine harvesters[J]. Computers and electronics in agriculture, 2024, 218: 108689.
[12] LIU M C, CHEN W B, CHENG J J, et al.Y-HRNet: Research on multi-category cherry tomato instance segmentation model based on improved YOLOv7 and HRNet fusion[J]. Computers and electronics in agriculture, 2024, 227: 109531.
[13] LIU Z H, HOU W S, CHEN W J, et al.The algorithm for foggy weather target detection based on YOLOv5 in complex scenes[J]. Complex & intelligent systems, 2024, 11(1): 71.
[14] HUANG L Y, TAN J T, CHEN Z H.Mamba-UAV-SegNet: A multi-scale adaptive feature fusion network for real-time semantic segmentation of UAV aerial imagery[J]. Drones, 2024, 8(11): 671.
[15] WU W B, HE Z K, LI J L, et al.Instance segmentation of tea garden roads based on an improved YOLOv8n-seg model[J]. Agriculture, 2024, 14(7): 1163.
[16] SHAMTA I, DEMIR F, DEMIR B E.Predictive fault detection and resolution using YOLOv8 segmentation model: A comprehensive study on hotspot faults and generalization challenges in computer vision[J]. Ain shams engineering journal, 2024, 15(12): 103148.
[17] 顾群,随思懿,王瑞,等.基于改进YOLOv8的皮肤黑色素瘤图像分割算法[J/OL].计算机工程,2024(2024-11-06). https://link.cnki.net/doi/10.19678/j.issn.1000-3428.0069910.
[18] 王译崧,华杭波,孔明,等.Rep-YOLOv8车辆行人检测分割算法[J].现代电子技术,2024,47(9):143-149.
[19] TANG H Y,LIANG S, YAO D, et al.A visual defect detection for optics lens based on the YOLOv5-C3CA-SPPF network model[J]. Optics express, 2023, 31(2): 2628-2643.
[20] LAU K W, PO L M, REHMAN Y A U. Large separable kernel attention: Rethinking the large kernel attention design in CNN[J]. Expert systems with applications, 2024, 236: 121352.