Detection of maize kernels breakage rate based on K-means clustering

Abstract

yangliangsia@sina.cnAbstract. In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Thermal analysis and artificial vision of laser irradiation on corn

“…Also, recent researches have demonstrated that patterns recognition technology is efficient technique to classify and identify objects and an important tool to review foods quality [20]. With the different tools generated for the image analysis it has been possible to apply it to perform analysis of grain and seed qualities [18,19,[21][22][23][24][25], and classification of seeds and fruits [5,6,[26][27][28][29], as well as the segmentation in shrimp structure [20], recognition of patterns in contamination, agricultural plants, livestock and food [4,8,[30][31][32][33]; thus it is also possible to identify diseases in different crops and rice [22,34]. By means of several techniques it is possible to determine and characterize agricultural samples to obtain different characteristics that they possess, from the experimental results that techniques generate such as signals, images, data, etc.…”