Measurement of harvesting width of intelligent combine harvester by improved probabilistic Hough transform algorithm

Wang, Lihui; Yang, Yu; Shi, Jiachen

doi:10.1016/j.measurement.2019.107130

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…Therefore, the more significant the difference in the motion trajectories between particles, the better the separation effect. If different magnetic particles are to be graded, it must be conducted in a non-uniform magnetic field [24][25][26] .…”

Section: Theoretical Model Of Cottonseed Magnetic Separationmentioning

confidence: 99%

Development of the magnetic separation device for surface cracks of delinted cottonseed

Du,

Wang,

Pang

et al. 2023

International Journal of Agricultural and Biological Engineering

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In seed processing for commercial production, the collision or friction between mechanical parts and seed is inevitable. These collisions or friction can cause cracks on the surface of the seed, which can affect germination rates and ultimately reduce crop yields. Using the difference of the seed motion trajectories with different crack degrees in the magnetic field, the grading of the surface crack seed can be realized. In this study, the motion law of seed in a non-uniform magnetic field was analyzed by taking the delinted cottonseed as an object. A magnetic separation device for crop seeds was developed. This device was essentially composed of the feed throat, magnetic roller, conveyor belt, and variable-frequency adjustablespeed motor. The magnetic powder adhering seeds enter the magnetic separation device through the feed throat and are conveyed to the magnetic roller through the conveyor belt. The magnetic roller has different adsorption forces on seeds with different degrees of cracking, and seeds fall into different outlets, thus completing the grading of the seeds. By adjusting working parameters such as the magnetic field strength and the speed of the conveyor belt, magnetic separation could be made. In order to verify the grading effect, the magnetic separation accuracy was taken as the inspection index, the magnetic powder's mesh number, the mass mixing ratio between magnetic powder and delinted cottonseed, and the rotation speed of the magnetic roller were taken as the inspection factors. The response surface methodology was used to optimize the working parameters of the experimental device. The results showed that the optimal process parameters of the magnetic separation device were as follows: the number of magnetic powder meshes was 250, the mass mixing ratio between magnetic powder and delinted cottonseed was 1:20, and when the rotational speed of the magnetic separation roller was 20 r/min, the detection rate reached 92.5%. The designed magnetic separation device can realize the non-destructive batch detection of seed surface cracks with high work efficiency and has guiding significance for the high-precision and low-damage classification detection and classification of commercial seed.

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Section: Theoretical Model Of Cottonseed Magnetic Separationmentioning

confidence: 99%

Development of the magnetic separation device for surface cracks of delinted cottonseed

Du,

Wang,

Pang

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

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“…The camera was mounted on the top left side of the intelligent harvester robot, the camera coordinate system was set as (O C , X C , Y C , Z C ), the coordinate origin O C was located in the camera optical center, the right side of the camera mirror was in the X C direction, parallel to the ground plane. The coordinate system of the intelligent harvester was (O, X, Y, Z), the coordinate origin O was the fixed endpoint of the left divider, where the X-axis was perpendicular to the driving direction of the harvester, parallel to the ground plane and pointing to the right side of the harvester, and the Y-axis was perpendicular to the X-axis and parallel to the ground plane and pointing to the front of the harvester (Wang et al, 2020).…”

Section: Navigation Line Target Region Acquisitionmentioning

confidence: 99%

Improved random sampling consensus algorithm for vision navigation of intelligent harvester robot

Yang

Qin

et al. 2020

Self Cite

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Purpose Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed. Design/methodology/approach First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing. Findings The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame. Originality/value In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

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“…Harvesting width measurement is of great significance to improve agricultural efficiency. As shown in Figure 1, to improve the efficiency of the intelligent harvesting robot, it is necessary to make the harvesting width equal to the width of the header (Wang et al, 2020). The header width D of the intelligent harvesting robot is constant, so the harvesting width can be calculated based on the distance d. The harvesting width W can be obtained by:…”

Section: Introductionmentioning

confidence: 99%

An improved particle filtering to locate the crop boundary of an unharvested region using vision

Wang

Qin

et al. 2020

Self Cite

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Purpose Accurately, positioning is a fundamental requirement for vision measurement systems. The calculation of the harvesting width can not only help farmers adjust the direction of the intelligent harvesting robot in time but also provide data support for future unmanned vehicles. Design/methodology/approach To make the length of each pixel equal, the image is restored to the aerial view in the world coordinate system. To solve the problem of too much calculation caused by too many particles, a certain number of particles are scattered near the crop boundary and the distribution regularities of particles’ weight are analyzed. Based on the analysis, a novel boundary positioning method is presented. In the meantime, to improve the robustness of the algorithm, the back-projection algorithm is also used for boundary positioning. Findings Experiments demonstrate that the proposed method could well meet the precision and real-time requirements with the measurement error within 55 mm. Originality/value In visual target tracking, using particle filtering, a rectangular is used to track the target and cannot obtain the boundary information. This paper studied the distribution of the particle set near the crop boundary and proposed an improved particle filtering algorithm. In the algorithm, a small amount of particles is used to determine the crop boundary and accurate positioning of the crop boundary is realized.

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Measurement of harvesting width of intelligent combine harvester by improved probabilistic Hough transform algorithm

Cited by 13 publications

References 12 publications

Development of the magnetic separation device for surface cracks of delinted cottonseed

Development of the magnetic separation device for surface cracks of delinted cottonseed

Improved random sampling consensus algorithm for vision navigation of intelligent harvester robot

An improved particle filtering to locate the crop boundary of an unharvested region using vision

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