Real-time detection of crop rows in maize fields based on autonomous extraction of ROI

Yang, Yang; Zhou, Yang; Yue, Xuan; Zhang, Gang; Wen, Xing Ping; Ma, Biao; Xu, Liangyuan; Chen, Liqing

doi:10.1016/j.eswa.2022.118826

Cited by 44 publications

(17 citation statements)

References 32 publications

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“…Due to the diverse types of agricultural production and complex processes, which lead to a large family of agricultural robots, agricultural robots have various end-effectors to accomplish their operational tasks [110]. To address the problems of the difficulty in using large sprayers in hilly orchards, mechanical damage to fruit trees, and a low deposition rate within the canopy of fruit trees, Bao et al [111] designed a new remotecontrolled cable-driven target spray robot based on a concentric tubular manipulator with six degrees of freedom and using a spring-hinged structure.…”

Section: Other Mechanisms For End-effectorsmentioning

confidence: 99%

Actuators and Sensors for Application in Agricultural Robots: A Review

et al. 2022

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In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future.

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Section: Other Mechanisms For End-effectorsmentioning

confidence: 99%

Actuators and Sensors for Application in Agricultural Robots: A Review

et al. 2022

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“…introduced an improved YOLOv5 network model and a centerline extraction algorithm for detecting straight and curved crop rows, specifically designed for rice seedlings. Nevertheless, this method did not consider the complexity introduced by different growth stages or environmental conditions ( Yang et al., 2023 ). proposed a method that combines the YOLOv5 network model, hyper-green method, and Otsu method.…”

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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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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“…The navigation system can safely guide the agricultural machinery along the desired path of its low price, complete information acquisition, and wide detection information [5,6]. Since most field crops are generally planted or cultivated in rows, the path of agricultural machines in the field follows an s-shaped curve along the crop rows, so the accurate identification and extraction of the centerline of the crop rows is crucial for realizing the visual navigation of agricultural machinery [7]. Accurate crop row detection and agricultural machinery trajectory planning can reduce the fuel consumption and production cost of agricultural machinery and improve the utilization rate and operation efficiency of farm machinery.…”

Section: Introductionmentioning

confidence: 99%

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

Shi,

Bai,

Zhou

et al. 2023

Agriculture

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Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines.

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Real-time detection of crop rows in maize fields based on autonomous extraction of ROI

Cited by 44 publications

References 32 publications

Actuators and Sensors for Application in Agricultural Robots: A Review

Actuators and Sensors for Application in Agricultural Robots: A Review

Improving the maize crop row navigation line recognition method of YOLOX

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

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