A precise crop row detection algorithm in complex farmland for unmanned agricultural machines

Ruan, Zhiwen; Chang, Pao-Chi; Cui, Shangqing; Luo, Jiaqi; Gao, Rui; Su, Zhongbin

doi:10.1016/j.biosystemseng.2023.06.010

Cited by 29 publications

(10 citation statements)

References 17 publications

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“…We implemented multiple technical improvements such as multi-scale prediction, attention mechanism modules, and loss function optimization, enabling the model to maintain high precision under various lighting conditions. Additionally, compared to the method by ( Ruan et al., 2023 ), which estimated crop row numbers using the DBSCAN algorithm, our research directly utilizes the improved YOLOX-Tiny model for crop localization, followed by least squares fitting for centerline identification, yielding better recognition results in shorter timeframes.…”

Section: Discussionmentioning

confidence: 99%

“…In a recent study conducted by ( Zhu et al., 2022 ), a weeding robot was designed and implemented for efficient weed control in maize fields. The weeding robot achieved an average detection rate of 92.45% for maize seedlings ( Ruan et al., 2023 ). proposed a novel crop row detection method for unmanned agricultural machinery based on YOLO-R.…”

Section: Introductionmentioning

confidence: 99%

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Improving the maize crop row navigation line recognition method of YOLOX

Gong,

Zhuang,

Wang

2024

Front. Plant Sci.

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The accurate identification of maize crop row navigation lines is crucial for the navigation of intelligent weeding machinery, yet it faces significant challenges due to lighting variations and complex environments. This study proposes an optimized version of the YOLOX-Tiny single-stage detection network model for accurately identifying maize crop row navigation lines. It incorporates adaptive illumination adjustment and multi-scale prediction to enhance dense target detection. Visual attention mechanisms, including Efficient Channel Attention and Cooperative Attention modules, are introduced to better extract maize features. A Fast Spatial Pyramid Pooling module is incorporated to improve target localization accuracy. The Coordinate Intersection over Union loss function is used to further enhance detection accuracy. Experimental results demonstrate that the improved YOLOX-Tiny model achieves an average precision of 92.2 %, with a detection time of 15.6 milliseconds. This represents a 16.4 % improvement over the original model while maintaining high accuracy. The proposed model has a reduced size of 18.6 MB, representing a 7.1 % reduction. It also incorporates the least squares method for accurately fitting crop rows. The model showcases efficiency in processing large amounts of data, achieving a comprehensive fitting time of 42 milliseconds and an average angular error of 0.59°. The improved YOLOX-Tiny model offers substantial support for the navigation of intelligent weeding machinery in practical applications, contributing to increased agricultural productivity and reduced usage of chemical herbicides.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the maize crop row navigation line recognition method of YOLOX

Gong,

Zhuang,

Wang

2024

Front. Plant Sci.

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“…Real-time positioning is a prerequisite for UAT to achieve path planning, path tracking, and motion control [9]. Satellite positioning enables the identification of farmland locations, work areas, operation deviation, and driving speed [10]. Positioning technologies include satellite positioning systems, laser radar systems, and onboard cameras.…”

Section: Positioning Technologymentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

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“…19 Ruan et al proposed the YOLO-R target detection algorithm based on YOLOv4, effectively reducing the number of references using depth-separable convolution. 20 Li et al devised a lightweight network based on YOLOv4, leveraging the GhostNet architecture, effectively reducing the network's parameters. 21 While most researchers typically concentrate on accuracy or lightweight enhancements, this paper takes a distinctive approach.…”

Section: Introductionmentioning

confidence: 99%

Lightweight target detection algorithm based on partial convolution

Chen,

Liu

2024

J. Electron. Imag.

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You only look once (YOLO) target detection algorithms have garnered substantial attention in engineering inspection tasks due to their remarkable accuracy and swift detection speed. However, adapting the YOLOv7 algorithm to engineering construction presents challenges in balancing lightweight design and high precision. This work introduces a YOLOv7 lightweight detection algorithm, leveraging partial convolution to efficiently reduce network parameters without compromising detection speed. Integrating partial convolution into the network's backbone is a highly effective parameter reduction strategy. Additionally, we propose the SE_Conv module following quantitative analyses through experiments, thereby significantly enhancing the algorithm's detection accuracy. To address the impact of low-quality data on detection effectiveness in engineering tasks, the advanced bounding box loss function WIoU is employed. In a practical application scenario involving helmet detection, the algorithm achieves a notable 23.4% reduction in parameters and a substantial 28.2% decrease in operations through partial convolution. Subsequent experiments with the SE_Conv module yield a noteworthy 1.4% improvement in results. Significantly, in head detection, our algorithm surpasses YOLOv8 algorithm by 2.6% and single shot multibox detector algorithm by 9.4%. These findings underscore the efficacy of our proposed algorithm in engineering applications.

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A precise crop row detection algorithm in complex farmland for unmanned agricultural machines

Cited by 29 publications

References 17 publications

Improving the maize crop row navigation line recognition method of YOLOX

Improving the maize crop row navigation line recognition method of YOLOX

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Lightweight target detection algorithm based on partial convolution

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