Navigation line detection algorithm for corn spraying robot based on improved LT-YOLOv10s

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2025-04-24 DOI:10.1007/s11119-025-10243-3

Zhihua Diao, Shushuai Ma, Jiangbo Li, Jingcheng Zhang, Xingyi Li, Suna Zhao, Yan He, Baohua Zhang, Liying Jiang

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引用次数: 0

Abstract

The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed a corn crop row navigation line recognition algorithm based on the LT-YOLOv10s model. By introducing lightweight network models (GhosNet), efficient feature pyramid models (SPPFA), and efficient feature attention modules (PSCA) into the YOLOv10s network, we have reduced the complexity of the model and significantly enhanced the detection efficiency of corn plants. Then, the algorithm precisely locates corn plants using the center points of detection boxes and accurately fits crop rows using the least squares method. Finally, the navigation lines centered on the corn crop rows are determined through the adjacent centerline method. Experimental data significantly demonstrates that the comprehensive performance of the LT-YOLOv10s model surpasses industry benchmark models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and the traditional YOLOv10s. The proposed algorithm for extracting the center navigation line of corn crop rows boasts an average fitting time of just 26ms with an accuracy rate of up to 93.8%, ensuring precision and reliability in navigation line extraction. This provides robust technical support for precise navigation of corn-spraying robots.

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基于改进LT-YOLOv10s的玉米喷洒机器人导航线检测算法

人工智能技术与农业的深度融合，极大地推动了智慧农业的快速发展。然而，该领域仍然面临着许多挑战，包括高算法复杂度和农田环境下有限的检测速度。为了解决玉米喷洒机器人在导航和识别线条方面遇到的挑战，我们提出了一种基于LT-YOLOv10s模型的玉米作物行导航线条识别算法。通过在YOLOv10s网络中引入轻量级网络模型（GhosNet）、高效特征金字塔模型（SPPFA）和高效特征关注模块（PSCA），降低了模型的复杂度，显著提高了玉米植株的检测效率。然后，利用检测盒中心点精确定位玉米植株，利用最小二乘法精确拟合作物行；最后，通过相邻中心线法确定以玉米作物行为中心的导航线。实验数据显著表明，LT-YOLOv10s模型的综合性能超过了YOLOv5s、YOLOv7、YOLOv8s、YOLOv9s以及传统的YOLOv10s等行业基准模型。提出的玉米作物行中心导航线提取算法平均拟合时间仅为26ms，准确率高达93.8%，保证了导航线提取的精度和可靠性。这为玉米喷洒机器人的精确导航提供了强有力的技术支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.