Extracting the navigation path of a tomato-cucumber greenhouse robot based on a median point Hough transform

Chen, Jiqing; Qiang, Hu; Wu, Jiahua; Xu, Guanwen; Wang, Zhikui; Liu, Xu

doi:10.1016/j.compag.2020.105472

Cited by 43 publications

(15 citation statements)

References 17 publications

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“…The predicted path of the field-weeding robot has a curved problem. If the navigation line is generated using the least squares method [ 24 ], the standard Hough transform [ 31 ], or the median-point Hough transform [ 21 ], the above three methods belong to the line-fitting detection method. Using a method of straight-line detection and fitting can cause the agricultural robot to damage and press the seedlings, which violates the robot’s intention of correct visual navigation.…”

Section: Resultsmentioning

confidence: 99%

“…The Hough transform [ 19 ] is one of the most widely used line-fitting methods and is particularly robust when dealing with discontinuous crop rows or fields with many weeds. Many applications of Hough-transform-based crop row recognition in automatic navigation can be found in the literature [ 5 , 20 , 21 , 22 ]. Least squares is a mathematical optimization technique that finds the best functional match to the data, minimizing the sum of squared deviations to fit a robot’s navigation path line.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation

Yue

et al. 2022

Sensors

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Automated robots are an important part of realizing sustainable food production in smart agriculture. Agricultural robots require a powerful and precise navigation system to be able to perform tasks in the field. Aiming at the problems of complex image background, as well as weed and light interference factors of the visual navigation system in field and greenhouse environments, a Faster-U-net model that retains the advantages of the U-net model feature jump connection is proposed. Based on the U-net model, pruning and optimization were carried out to predict crop ridges. Firstly, a corn dataset was trained to obtain the weight of the corn dataset. Then, the training weight of the obtained corn dataset was used as the pretraining weight for the cucumber, wheat, and tomato datasets, respectively. The three datasets were trained separately. Finally, the navigation line between ridges and the yaw angle of the robot were generated by B-spline curve fitting. The experimental results showed that the parameters of the improved path segmentation model were reduced by 65.86%, and the mPA was 97.39%. The recognition accuracy MIoU of the Faster-U-net model for maize, tomatoes, cucumbers, and wheat was 93.86%, 94.01%, 93.14%, and 89.10%, respectively. The processing speed of the single-core CPU was 22.32 fps/s. The proposed method had strong robustness in predicting rows of different crops. The average angle difference of the navigation line under a ridge environment such as that for corn, tomatoes, cucumbers, or wheat was 0.624°, 0.556°, 0.526°, and 0.999°, respectively. This research can provide technical support and reference for the research and development of intelligent agricultural robot navigation equipment in the field.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation

Yue

et al. 2022

Sensors

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“…After obtaining the binary image such as Figure 3d, it is crucial to determine the information about crop rows. Usually combining the central point extraction method and the Hough transform [1,5,10,[20][21][22][23][30][31][32][33][34][35][36], international scholars have dealt with this field. Besides, a large number of researchers have extracted edges for tracking the target location [37][38][39][40][41][42].…”

Section: Comparison Of Several Methods For Determining the Crop Row I...mentioning

confidence: 99%

Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

Zhao,

Yuan,

Yang

et al. 2023

IET Image Processing

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More accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, Canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (Canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the Canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K‐means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of Canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods.

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“…Vision sensor is an imaging device, which can quickly collect image information and process the information. For instance, HT [21][22][23] and PPHT [24][25][26][27] are commonly used line detection algorithms of vision sensor. Both methods have good anti-interference ability, and have become important methods of line detection and computer image recognition.…”

Section: Introductionmentioning

confidence: 99%

Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation

Zheng

Cui

et al. 2023

IET Control Theory & Appl

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In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy.

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Extracting the navigation path of a tomato-cucumber greenhouse robot based on a median point Hough transform

Cited by 43 publications

References 17 publications

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation

Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation

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