Seedling Stage Corn Line Detection Method Based on Improved YOLOv8

doi:10.12133/j.smartag.SA202408008

Abstract

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

CLC Number:

TP181

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.

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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