Size Grading of Corn Kernels With Machine Vision

Ni, B; Paulsen, Marvin R; Reid, John F.

doi:10.13031/2013.19408

Cited by 13 publications

(1 citation statement)

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“…Zayas et al used image processing and pattern recognition to identify whole and broken corn kernels [5]. Ni et al developed a prototype system to classify whole and broken kernels [6], corn kernels based on their crown shape [7], and grade corn based on their size [8]. Luo et al used computer vision technology to separate six types of wheat kernels based on their color features [9].…”

Section: Introductionmentioning

confidence: 99%

Corn Classification System based on Computer Vision

Dai

Sun

et al. 2019

Symmetry

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Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.

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Section: Introductionmentioning

confidence: 99%

Corn Classification System based on Computer Vision

Dai

Sun

et al. 2019

Symmetry

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The State of Motion Stereo About Plant Leaves Monitoring System Design and Simulation

Fan

Guo

Wang

et al. 2019

Computer and Computing Technologies in Agriculture XI

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A binocular stereo vision plant leaf motion monitoring system was proposed in this paper, the system includes a binocular camera, horizontal movement module, the vertical movement module, the image acquisition card, and a computer. The supporting structure holds camera above the measured maize leaf, the camera is able to capture image pair at 30fps, An image processing program is installed in computer, the program includes image acquisition, image pre-processing, markers extraction, sub-pixel edge refinement, 3D reconstruction and other modules. A fluorescent ball(diameter 0.35cm )with high reflectivity was chosen as a marker, and its intensity is higher than the background environment which makes it easier to extract contour of ball out of background. The spherical marker will keep its circular shape more or less after perspective projection. In order to further improve the accuracy of stereo matching, a sub-pixel edge detection method based on gradient magnitude was adopted, in the initial position of the edge, a set of reference points was selected according to the gradient magnitude threshold along gradient direction, the x and y coordination of reference points sum up weighted by gradient magnitude, the mean of weighted sum is the increments of initial edge in sub-pixel form. In the simulation experiment, the camera is set away from the measured object about 50cm, the system measurement accuracy can reach to 0.0139cm, it is able to detect the small changes of leaf position. In field experiments, the actual measurement of the movement leaf caused by growth and physiological responses achieved the desired results, this study provide a solution to continuous, non-destructive, non-contact acquire crop growth information in three-dimensional space.

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