基于改进YOLOv8的苗期玉米行检测方法

doi:10.12133/j.smartag.SA202408008

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (6): 72-84.doi: 10.12133/j.smartag.SA202408008

• 专题--农业知识智能服务和智慧无人农场（上） • 上一篇下一篇

基于改进YOLOv8的苗期玉米行检测方法

李洪波¹^,²(), 田鑫¹^,²(), 阮志文¹^,², 刘少文¹^,², 任玮琪¹^,², 苏中滨¹^,²(), 高睿¹^,², 孔庆明¹^,²

^1. 东北农业大学电气与信息学院，黑龙江哈尔滨 150030，中国
^2. 黑龙江省农业农村部东北智慧农业技术重点实验室，黑龙江哈尔滨 150030，中国

收稿日期:2024-08-13 出版日期:2024-11-30
作者简介:
田鑫，研究方向为智能视觉感知。E-mail： tianxin991218@163.com
李洪波和田鑫对本文有同等贡献，并列第一作者。
通信作者:
李洪波，硕士，助教，研究方向为智能视觉感知。E-mail：lihongbo19790127@126.com；
苏中滨，博士，教授，研究方向为智慧农业。E-mail：suzb001@163.com

Seedling Stage Corn Line Detection Method Based on Improved YOLOv8

LI Hongbo¹^,²(), TIAN Xin¹^,²(), RUAN Zhiwen¹^,², LIU Shaowen¹^,², REN Weiqi¹^,², SU Zhongbin¹^,²(), GAO Rui¹^,², KONG Qingming¹^,²

^1. Institutions of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
^2. Key Laboratory of Northeast Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Heilongjiang Province, Harbin 150030, China

Received:2024-08-13 Online:2024-11-30
Foundation items:The National Science and Technology Innovation 2030 of New Generation of Artificial Intelligence Major Project(2021ZD0110904)
About author:
TIAN Xin, E-mail: tianxin991218@163.com
LI Hongbo and TIAN Xin contributed equally to this work
Corresponding author:
LI Hongbo, E-mail: lihongbo19790127@126.com;
SU Zhongbin, E-mail: suzb001@163.com

摘要/Abstract

摘要：

［目的/意义］ 智能农机是田间机器人发展的新趋势。作物行提取是智能农机自主作业的重要环节，对于提高田间作业效率、减少作物损害、优化资源利用具有重要意义。然而，在复杂的田间环境中，如强烈的光照和杂草干扰，传统的作物行检测方法往往难以达到高精度和高效率。为了应对这些挑战，本研究旨在提高无人农机在复杂光照和杂草干扰下的苗期玉米行检测精度与效率，从而减少作物损害。 ［方法］ 提出一种基于YOLOv8-G的作物行检测方法，结合了YOLOv8-G目标检测算法、亲和传播聚类算法，以及最小二乘法。YOLOv8-G是在YOLOv8和GhostNetV2基础上改进的轻量级目标检测算法，通过提取玉米苗的中心点位置，利用亲和传播算法进行聚类分析，并通过最小二乘法拟合作物行。［结果与讨论］ YOLOV8-G算法在玉米苗期的7天、14天和21天时的平均准确率（Average Precision, AP）分别为98.22%、98.15%和97.32%。该算法在玉米苗期的作物行提取准确率达到96.52%。相比传统检测方法，YOLOv8-G在处理复杂背景和强光照条件下表现更为优异，且计算效率有一定提升。 ［结论］ 提出的基于YOLOv8-G的作物行检测方法能够在复杂光照条件和杂草干扰下快速准确地识别田间作物并模拟协同目标行，不仅为无人农机的自动导航提供有力支持，还能高效适配嵌入式设备，在提升农业自动化、减少人工操作和降低作物损害的同时，为智能农机的实时作业提供技术保障，具有重要的应用价值。

关键词: 作物行检测, YOLOv8-G, 主干网络, 亲和传播, 最小二乘法

Abstract:

[Objective] Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field. However, traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions, such as strong light exposure and weed interference. The aims are to develop an effective crop line extraction method by combining YOLOv8-G, Affinity Propagation, and the Least Squares method to enhance detection accuracy and performance in complex field environments. [Methods] The proposed method employs machine vision techniques to address common field challenges. YOLOv8-G, an improved object detection algorithm that combines YOLOv8 and GhostNetV2 for lightweight, high-speed performance, was used to detect the central points of crops. These points were then clustered using the Affinity Propagation algorithm, followed by the application of the Least Squares method to extract the crop lines. Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework, and ablation studies were performed to validate the enhancements made in YOLOv8-G. [Results and Discussions] The performance of the proposed method was compared with classical object detection and clustering algorithms. The YOLOv8-G algorithm achieved average precision (AP) values of 98.22%, 98.15%, and 97.32% for corn detection at 7, 14, and 21 days after emergence, respectively. Additionally, the crop line extraction accuracy across all stages was 96.52%. These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field. [Conclusions] The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference, enabling rapid and accurate crop identification. This approach supports the automatic navigation of agricultural machinery, offering significant improvements in the precision and efficiency of field operations.

Key words: crop row detection, YOLOv8-G, backbone, affinity propagation, least square method

中图分类号:

TP181

李洪波, 田鑫, 阮志文, 刘少文, 任玮琪, 苏中滨, 高睿, 孔庆明. 基于改进YOLOv8的苗期玉米行检测方法[J]. 智慧农业(中英文), 2024, 6(6): 72-84.

LI Hongbo, TIAN Xin, RUAN Zhiwen, LIU Shaowen, REN Weiqi, SU Zhongbin, GAO Rui, KONG Qingming. Seedling Stage Corn Line Detection Method Based on Improved YOLOv8[J]. Smart Agriculture, 2024, 6(6): 72-84.

图/表 16

参考文献 32

1	SHI J Y, BAI Y H, DIAO Z H, et al. Row detection BASED navigation and guidance for agricultural robots and autonomous vehicles in row-crop fields: Methods and applications[J]. Agronomy, 2023, 13(7): ID 1780.
2	QU F H, DING T Y, ZHENG X M, et al. Verification of farmland crop row direction recognition method based on plot morphological characteristics[J]. Remote sensing technology and application, 2024, 39(5): 1213-1222.
3	BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. arXiv: 2004.10934, 2020.
4	YANG Z L, YANG Y, LI C R, et al. Tasseled crop rows detection based on micro-region of interest and logarithmic transformation[J]. Frontiers in plant science, 2022, 13: ID 916474.
5	FU D B, JIANG Q, QI L, et al. Detection of the centerline of rice seedling belts based on region growth sequential clustering-RANSAC[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(7): 47-57.
6	ZHAI Z Q, XIONG K, WANG L, et al. Crop row detection and tracking based on binocular vision and adaptive Kalman filter[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(8): 143-151.
7	PONNAMBALAM V R, BAKKEN M, MOORE R J D, et al. Autonomous crop row guidance using adaptive multi-ROI in strawberry fields[J]. Sensors, 2020, 20(18): ID 5249.
8	LIU X, HU C H, LI P P. Automatic segmentation of overlapped poplar seedling leaves combining Mask R-CNN and DBSCAN[J]. Computers and electronics in agriculture, 2020, 178: ID 105753.
9	DE SILVA R, CIELNIAK G, WANG G, et al. Deep learning-based crop row detection for infield navigation of agri-robots[J]. Journal of field robotics, 2024, 41(7): 2299-2321.
10	DE SILVA R, CIELNIAK G, GAO J F, et al. Towards agricultural autonomy: Crop row detection under varying field conditions using deep learning[EB/OL]. arXiv:2109.08247, 2021.
11	WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2023: 7464-7475.
12	ZHENG Z Y, LI J W, QIN L F. YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows[J]. Computers and electronics in agriculture, 2023, 209: ID 107857.
13	ZHANG L M, LIU G W, QI Y D, et al. Research progress on key technologies of agricultural machinery unmanned driving system[J]. Journal of intelligent agricultural mechanization, 2022, 3(1): 27-36.
14	CUI X Y, CUI B B, MA Z, et al. Integration of geometric-based path tracking controller and its application in agricultural machinery automatic navigation[J]. Journal of intelligent agricultural mechanization, 2023, 4(3): 24-31.
15	Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]// Computer Vision–ECCV 2016: 14th European Conference. Berlin, Germany: Springer, 2016: 21-37.
16	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
17	BOOGAARD F P, RONGEN K S A H, KOOTSTRA G W. Robust node detection and tracking in fruit-vegetable crops using deep learning and multi-view imaging[J]. Biosystems engineering, 2020, 192: 117-132.
18	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. arXiv: 1409.1556, 2014.
19	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2016: 770-778.
20	HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. arXiv: 1704.04861, 2017.
21	SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: Inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2018: 4510-4520.
22	HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2019: 1314-1324.
23	HAN K, WANG Y H, TIAN Q, et al. GhostNet: More features from cheap operations[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2020: 1580-1589.
24	TANG Y, HAN K, GUO J, et al. GhostNetv2: Enhance cheap operation with long-range attention[J]. Advances in Neural Information Processing Systems, 2022, 35: 9969-9982.
25	SHI J Y, BAI Y H, ZHOU J, et al. Multi-crop navigation line extraction based on improved YOLOv8 and threshold-DBSCAN under complex agricultural environments[J]. Agriculture, 2023, 14(1): ID 45.
26	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2017: 2980-2988.
27	CHI J, GUO S, ZHANG H, et al. L-GhostNet: Extract better quality features[J]. IEEE Access, 2023, 11: 2361-2374.
28	LIU F C, YANG Y, ZENG Y M, et al. Bending diagnosis of rice seedling lines and guidance line extraction of automatic weeding equipment in paddy field[J]. Mechanical systems and signal processing, 2020, 142: ID 106791.
29	GARCÍA-SANTILLÁN I D, PAJARES G. On-line crop/weed discrimination through the Mahalanobis distance from images in maize fields[J]. Biosystems engineering, 2018, 166: 28-43.
30	DIAO Z H, GUO P L, ZHANG B H, et al. Maize crop row recognition algorithm based on improved UNet network[J]. Computers and electronics in agriculture, 2023, 210: ID 107940.
31	NAN Y L, ZHANG H C, ZENG Y, et al. Intelligent detection of Multi-Class pitaya fruits in target picking row based on WGB-YOLO network[J]. Computers and electronics in agriculture, 2023, 208: ID 107780.
32	CHEN J Q, QIANG H, WU J H, et al. Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform[J]. Computers and electronics in agriculture, 2021, 180: ID 105911.

Data	Collection time	Number of images with a depression angle of 60°	Number of images with a depression angle of 70°	Number of images with a depression angle of 80°	Number of images with a depression angle of 90°	Total number of images
Data1	7 days after corn emergence	100	100	100	100	400
Data2	14 days after corn emergence	100	100	100	100	400
Data3	21 days after corn emergence	100	100	100	100	400
Data4	Contains Data 1， Data 2， Data 3	300	300	300	300	1 200

GNV2	DCN	FL	CA	Params/M	GFLOPS/G	FPS	AP/%
×	×	×	×	35.785	98.121	12.53	98.92
√	×	×	×	30.238	42.349	16.29	98.74
√	√	×	×	7.814	9.712	27.93	96.67
√	√	√	×	7.824	9.691	27.21	97.23
√	√	√	√	8.197	9.835	27.13	98.22

YOLOv8-Backbone	Params/M	GFLOPS/G	FPS	AP/%
GhostNetV2	29.418	43.214	16.80	96.50
GhostNet	28.753	41.836	18.52	96.45
MobilenetV1	30.132	52.146	18.28	94.80
MobilenetV2	27.618	44.913	18.36	95.76
MobilenetV3	28.442	44.352	18.05	96.05
Resnet50	52.496	113.446	11.43	96.21
VGG	42.593	287.169	6.29	92.14

Models	Param/M	GFLOPS/G	FPS
YOLOv8-G	8.20	9.36	27.13
YOLOv8-m	35.75	55.48	23.71
YOLOv7（standard version）	37.62	59.96	13.53
YOLOv4（standard version）	63.94	106.47	10.93
Faster R-CNN+MobileNet	26.28	940.92	3.00
SSD（standard version）	23.61	68.76	24.07

Dataset	AP value/%	Standard deviations
Data1	98.22	6.50
Data2	98.15	6.48
Data3	97.32	6.45
Data4	96.50	6.39

基于改进YOLOv8的苗期玉米行检测方法

Seedling Stage Corn Line Detection Method Based on Improved YOLOv8

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 32

相关文章 4

编辑推荐

Metrics

本文评价

Algorithm	Period	Params/M	GFLOPS/G	FPS	Accuracy/%
DBSCAN	Data 1	12.5	35.6	24.5	96.51
	Data 2	12.5	35.6	24.5	98.86
	Data 3	12.5	35.6	24.5	95.85
	Data 4	12.5	35.6	24.5	97.07
OPTICS	Data 1	14.2	39.8	20.3	94.04
	Data 2	14.2	39.8	20.3	94.51
	Data 3	14.2	39.8	20.3	95.85
	Data 4	14.2	39.8	20.3	95.14
Affinity Propagation	Data 1	10.8	31.7	26.1	95.27
	Data 2	10.8	31.7	26.1	97.52
	Data 3	10.8	31.7	26.1	97.31
	Data 4	10.8	31.7	26.1	96.52

[1]	黎祖胜, 唐吉深, 匡迎春. 基于改进YOLOv10n的轻量化荔枝虫害小目标检测模型[J]. 智慧农业(中英文), 2025, (): 1-14.
[2]	靳学萌, 梁西银, 邓鹏飞. 基于改进YOLOv10的轻量级黄花菜分级检测模型[J]. 智慧农业(中英文), 2024, 6(5): 108-118.
[3]	毛克彪, 张晨阳, 施建成, 王旭明, 郭中华, 李春树, 董立新, 吴门新, 孙瑞静, 武胜利, 姬大彬, 蒋玲梅, 赵天杰, 邱玉宝, 杜永明, 徐同仁. 基于人工智能的地球物理参数反演范式理论及判定条件[J]. 智慧农业(中英文), 2023, 5(2): 161-171.
[4]	夏雪, 柴秀娟, 张凝, 周硕, 孙琦鑫, 孙坦. 用于边缘计算设备的果树挂果量轻量化估测模型[J]. 智慧农业(中英文), 2023, 5(2): 1-12.

Model	Data1/%	Data2/%	Data3/%	Data4/%
YOLOv8-G	98.22	98.15	97.32	96.50
YOLOv8	99.13	98.70	98.54	97.11
YOLOv7	99.71	98.50	98.92	96.83
YOLOv4	97.81	94.18	95.73	92.69
Faster R-CNN	95.12	96.48	97.60	92.80
SSD	91.12	94.41	95.31	89.51