Autonomous detection of crop rows based on adaptive multi-ROI in 
maize fields

Zhang, Yang; Yang, Yang; Zhang, Boli; Wen, Xing Ping; Yue, Xuan; Chen, Liqing

doi:10.25165/j.ijabe.20211404.6315

Cited by 17 publications

(19 citation statements)

References 30 publications

(41 reference statements)

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“…To further verify the reliability and real-time performance of this study it is compared with the methods proposed by Hough ( 1962 ) (Hough Transform) (Algorithm-1), Ji and Qi ( 2011 ) (Algorithm-2), Zhou et al ( 2021 ) (Algorithm-3), and Zhang et al ( 2018b ) (Algorithm-4). Additionally, we will analyze the results of the performance among different methods in terms of accuracy and processing time.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…Crop contours in each strip are calculated, and their geometric centers are extracted as feature points for fitting. Zhou et al ( 2021 ) determined the multi-ROI by dividing the horizontal strips. The initial ROI is calculated and continuously slides upward for the update.…”

Section: Introductionmentioning

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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“…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%

“…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. For example, Zhang et al [ 7 ], Song et al [ 23 ], and Zhou et al [ 24 ] used the least squares method or its variants to fit maize crop rows and used them in a visual navigation system for robots.…”

Section: Introductionmentioning

confidence: 99%

“…According to the above methods, this research analyzes and condenses a crop ridge detection method based on selecting the driving area as the area of interest. Zhou et al [ 24 ] selected the drivable area of interest for a plant protection robot with traditional image-processing methods, but the applicability for multicrop environments, such as tomatoes, cucumbers, and wheat, is still unknown. Aiming at this problem, an adaptive multiple regions of interest agricultural robot navigation path recognition method based on deep-learning semantic segmentation is proposed.…”

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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QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions

Islam,

Ullah,

Bais

2023

Neural Comput & Applic

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Autonomous detection of crop rows based on adaptive multi-ROI in maize fields

Cited by 17 publications

References 30 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

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

QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions

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