Extraction of navigation line based on improved grayscale factor in corn field

Chen, Jiqing; Qiang, Hu; Xu, Guanwen; Liu, Xu; Mo, Rongxian; Huang, Renzhi

doi:10.1590/0103-8478cr20190699

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The grayscale processing (Liu et al, 2010 , 2020 ; Laursen et al, 2014 ) is mainly performed using the excess green (ExG) (Comba et al, 2015 ; Tang et al, 2016 ; Chen et al, 2020 ) feature operator when segmenting the crops with the background at the early stage. However, ExG was not effective in processing maize at the tasseling location.…”

Section: Methodsmentioning

confidence: 99%

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation

Yang

et al. 2022

Front. Plant Sci.

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Machine vision-based navigation in the maize field is significant for intelligent agriculture. Therefore, precision detection of the tasseled crop rows for navigation of agricultural machinery with an accurate and fast method remains an open question. In this article, we propose a new crop rows detection method at the tasseling stage of maize fields for agrarian machinery navigation. The whole work is achieved mainly through image augment and feature point extraction by micro-region of interest (micro-ROI). In the proposed method, we first augment the distinction between the tassels and background by the logarithmic transformation in RGB color space, and then the image is transformed to hue-saturation-value (HSV) space to extract the tassels. Second, the ROI is approximately selected and updated using the bounding box until the multiple-region of interest (multi-ROI) is determined. We further propose a feature points extraction method based on micro-ROI and the feature points are used to calculate the crop rows detection lines. Finally, the bisector of the acute angle formed by the two detection lines is used as the field navigation line. The experimental results show that the algorithm proposed has good robustness and can accurately detect crop rows. Compared with other existing methods, our method's accuracy and real-time performance have improved by about 5 and 62.3%, respectively, which can meet the accuracy and real-time requirements of agricultural vehicles' navigation in maize fields.

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Section: Methodsmentioning

confidence: 99%

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation

Yang

et al. 2022

Front. Plant Sci.

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“…In the agricultural sector, particularly in crop fields, vision-based systems are favored due to their costeffectiveness when compared to LiDARs and radars. The employment of vision-based systems has been extensively suggested as a potential solution for this task (Yang et al, 2018;Chen et al, 2020;Liu et al, 2016). (Yang et al, 2018) presented a vision system capable of filtering images based on the red color to detect the visible roots of maize plants.…”

Section: Related Workmentioning

confidence: 99%

“…This detection was then refined using the least square method to determine the optimal path to be followed. In a similar vein, (Chen et al, 2020) proposed an alternative approach by employing the Hough transformation to identify the central navigation line. Another variation was introduced by (Liu et al, 2016), who developed a monocular vision navigation system for maize canopy based on RBF (Radial Basis Function).…”

Section: Related Workmentioning

confidence: 99%

Rockerbot

Zinzani,

Usuelli,

Cudrano

et al. 2024

agricsci

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Crop inspection plays a significant role in modern agricultural practices as it enables farmers to evaluate the condition of their fields and make informed decisions regarding crop management. However, existing methods of crop inspection are often labor-intensive, leading to slow and costly processes. Therefore, there is a pressing need for more efficient and cost-effective approaches to crop inspection to improve agricultural productivity, sustainability, and to deal with labor shortage. In this study, we present Rockerbot, a novel agricultural robot designed as a compact rover capable of navigating and surveying maize fields in their early growth stages. This technology is essential for timely landscape adjustments to ensure optimal crop production. The document offers a comprehensive review of the decisions made during the hardware and software development stages. The hardware section is centered around design choices influenced by the rover’s kinematics, while the software section outlines the tasks that Rockerbot can perform using mobile perception, such as mapping, sensing, and detection.

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“…Denoising the extracted targets to determine the crop points. The final navigation line is obtained by fitting the feature points [14][15][16]. The single crop detection algorithm detects crops in images and is fast.…”

Section: Introductionmentioning

confidence: 99%

Cotton Stubble Detection Based on Improved YOLOv3

Yang

Nie

et al. 2023

Agronomy

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The stubble after cotton harvesting was used as the detection object to achieve the visual navigation operation for residual film recovery after autumn. An improved (You Only Look Once v3) YOLOv3-based target detection algorithm was proposed to detect cotton stubble. First, field images of residual film recycling were collected. Considering the inconsistency between stubble size and shape, a segmented labeling data set of stubble is proposed. Secondly, the Darknet-53 backbone of the original YOLOv3 network is improved to accommodate tiny targets. Next, the prediction anchor box of the improved detection backbone is clustered using K-means++, and the size of the prediction anchor box suitable for improved YOLOv3 is determined. Finally, for the false detection points after detection, a mean value denoising method is used to remove the false detection points. Feature points are extracted from the denoised stubble, and the candidate points are fitted by the least square method to obtain the navigation line. The optimal model with a mean average precision (mAP) of 0.925 is selected for testing at the test stage. The test results show that the algorithm in this article can detect the stubble of residual film recovery images at different locations, different time periods, and different camera depression angles without misdetection. The detection speed of a single image is 98.6 ms. Based on an improvement over YOLOv3, the improved model has a significantly higher detection rate in different scenarios than YOLOv3. This can provide practical technical support for the visual navigation of residual film recovery.

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Extraction of navigation line based on improved grayscale factor in corn field

Cited by 6 publications

References 11 publications

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation

Rockerbot

Cotton Stubble Detection Based on Improved YOLOv3

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