The seedling line extraction of automatic weeding machinery in paddy field

Wang, Shanshan; Yu, Shanshan; Zhang, Wenyi; Wang, Xingsong; Li, Jie

doi:10.1016/j.compag.2023.107648

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…(Vatn et al, 2020). Additionally, the familiarity and high level of trust established through multiple transactions between farmers and outsourcing service providers in the application process contribute to green and lowcarbon production (Zhang et al, 2023). However, due to the imperfect development of China's outsourcing market, especially in the construction of the outsourcing service system in northwest China, non-professional outsourcing services struggle to facilitate farmers' green and low-carbon production.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…Regarding weeding, as farmers pursue green production, which requires a significant labor force (Wang et al, 2023), manual weeding becomes more labor-intensive compared to herbicide application. However, manual weeding is limited by labor availability and cost.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…China urgently needs the intervention of third-party organizations to build a robust service mechanism . Existing researches on pesticide reduction primarily foucus on third-party entities such as cooperatives, social services, and financing services (Sun et al, 2019;Levesque et al, 2021;Yu et al, 2021;Zhang et al, 2023;Tambo et al, 2023). The 14th Five-Year Plan for the Green Development of Agriculture in China emphasizes the cultivation of social service organizations and professional cooperatives to promote pesticide reduction and increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Impact of pesticide outsourcing services on farmers’ low-carbon production behavior

Yang

et al. 2023

Front. Environ. Sci.

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Introduction: Promoting low-carbon development in agriculture is crucial for achieving agricultural modernization. One practical issue worth studying is whether outsourcing services can encourage farmers to adopt low-carbon production practices. This study analyzes the impact of pesticide outsourcing services on the low-carbon production behavior of farmers to provide China with practical recommendations.Methods: This empirical study investigates the impact of pesticide outsourcing services on farmers’ low-carbon production behavior using survey data from 450 rice growers in the Ningxia and Shaanxi provinces by endogenous switching regressions (ESR) model.Results and Discussion: Results showed that 1) outsourcing services have a significant negative impact on farmers’ manual weeding behavior, leading to a reduction in the frequency of manual weeding; 2) outsourcing services have a significant positive impact on farmers’ herbicide application behavior. In other words, participation in outsourcing leads to excessive pesticide application; 3) outsourcing services do not support a green and low-carbon production model where manual weeding replaces herbicide application. Due to the imperfect development of the outsourcing market in China, especially in the northwest region, the construction of outsourcing service system is lagging, and it is difficult for non-professional outsourcing services to play a driving role in green and low-carbon production for farmers, who will often choose the lower-cost mechanical application for maximum profit. The policy implication of this study is the need for a comprehensive and objective understanding of the impact and role of pesticide outsourcing services on farmers’ low-carbon production behavior. This understanding can help improve the market, policy, and other external environments for farmers to participate in outsourcing, ultimately promoting the sustainable development of green and low-carbon agriculture. This paper adds to the discussion of pesticide outsourcing services and farmers’ low-carbon production by drawing different conclusions from previous studies, providing a fresh foundation for policy-making.

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Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of pesticide outsourcing services on farmers’ low-carbon production behavior

Yang

et al. 2023

Front. Environ. Sci.

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“…Khan et al [21] introduced the ResNet-101 network into the Faster RCNN model and optimized the anchor point (pre-selected box) and feature extraction modules to achieve accurate detection of pea and strawberry crops in a real agricultural environment. To address the effect of random weed distribution on rice seedling row identification, Wang et al [22] proposed an improved PosiFocalLoss-Yolov5 (PFL-Yolov5) network to automatically identify rice seedling locations, and then the sub-region growing and outlier removal algorithms were used to fit the seedling row centerlines. Aiming at different growth periods and growing environments of maize crops, Diao et al [23] proposed a new spatial pyramid pooling structure to improve the YOLOv8s network, which improves the detection accuracy of maize plant cores, and enhances the adaptability of navigational line extraction in the complex farmland environment.…”

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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“…However, the authors did not integrate the detection results with path planning, and deployment on embedded hardware was not considered. Shanshan et al (2023) combined an improved YOLOv5 network with an improved centerline extraction algorithm to detect straight and curved crop rows, yet the method is only applicable to the seedling stage of rice. The aforementioned deep learning-based navigation methods can solve the real-time and robustness problems of navigation in numerous scenarios, however they are not effective for the navigation problem in the Panax ginseng shade house environment.…”

Section: Introductionmentioning

confidence: 99%

Navigation path extraction for inter-row robots in Panax notoginseng shade house based on Im-YOLOv5s

Tan,

Su,

Zhao

et al. 2023

Front. Plant Sci.

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IntroductionThe accurate extraction of navigation paths is crucial for the automated navigation of agricultural robots. Navigation line extraction in complex environments such as Panax notoginseng shade house can be challenging due to factors including similar colors between the fork rows and soil, and the shadows cast by shade nets.MethodsIn this paper, we propose a new method for navigation line extraction based on deep learning and least squares (DL-LS) algorithms. We improve the YOLOv5s algorithm by introducing MobileNetv3 and ECANet. The trained model detects the seven-fork roots in the effective area between rows and uses the root point substitution method to determine the coordinates of the localization base points of the seven-fork root points. The seven-fork column lines on both sides of the plant monopoly are fitted using the least squares method.ResultsThe experimental results indicate that Im-YOLOv5s achieves higher detection performance than other detection models. Through these improvements, Im-YOLOv5s achieves a mAP (mean Average Precision) of 94.9%. Compared to YOLOv5s, Im-YOLOv5s improves the average accuracy and frame rate by 1.9% and 27.7%, respectively, and the weight size is reduced by 47.9%. The results also reveal the ability of DL-LS to accurately extract seven-fork row lines, with a maximum deviation of the navigation baseline row direction of 1.64°, meeting the requirements of robot navigation line extraction.DiscussionThe results shows that compared to existing models, this model is more effective in detecting the seven-fork roots in images, and the computational complexity of the model is smaller. Our proposed method provides a basis for the intelligent mechanization of Panax notoginseng planting.

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The seedling line extraction of automatic weeding machinery in paddy field

Cited by 14 publications

References 22 publications

Impact of pesticide outsourcing services on farmers’ low-carbon production behavior

Impact of pesticide outsourcing services on farmers’ low-carbon production behavior

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

Navigation path extraction for inter-row robots in Panax notoginseng shade house based on Im-YOLOv5s

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