Multispectral image based germination detection of potato by using supervised multiple threshold segmentation model and Canny edge detector

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@Article{YANG:2021:CEA,

• author = "Yu Yang and Xin Zhao and Min Huang and Xin Wang2 and Qibing Zhu",
• title = "Multispectral image based germination detection of potato by using supervised multiple threshold segmentation model and {Canny} edge detector",
• journal = "Computers and Electronics in Agriculture",
• volume = "182",
• pages = "106041",
• year = "2021",
• ISSN = "0168-1699",
• DOI = "doi:10.1016/j.compag.2021.106041",
• URL = "https://www.sciencedirect.com/science/article/pii/S0168169921000594",
• keywords = "genetic algorithms, genetic programming, Potato germination detection, Multispectral image, HF-GP, SMTSM, Canny edge detector",
• abstract = "Whether from the perspective of agricultural production or food safety, potato germination detection is of great significance. Since the features (color, texture and context) of the germination area are similar to those of the non-germination area, the existing vision frameworks are difficult to accurately detect the germinations on the surface of potatoes. In this study, the method for detecting potato germination based on multispectral image combined with supervised multiple threshold segmentation model (SMTSM) and Canny edge detector was proposed. The SMTSM based on Genetic Programming algorithm combined with a hybrid fitness function (HF-GP) was used to transform the original multispectral images into multiple 2-D images for improving the contrast between region of interest (ROI) and background. A sub-mask of each transformed image was constructed using optimal segmentation threshold, and all of sub-masks were merged through pixel-multiplication to obtain segmentation mask. Meanwhile, in order to filter out the boundless areas that are misidentified as germinations, Canny edge detector was used on gray image to obtain edge mask. Finally, the segmentation mask and the edge mask were combined to complete the detection of germination of potato. Experimental results shown that the proposed method achieved the TPR of 90.91percent and the precision of 89.28percent for the edible potatoes, which were 4.17-19.05percent and 12.39-24.62percent higher than the competitive detectors in TPR and precision respectively. For the breeding potatoes, the proposed method with 89.67percent of TPR and 86.37percent of precision was 9.74-24.58percent and 15.70-20.39percent better than the competitors in TPR and precision respectively. The comparison confirms the proposed method has excellent detection effect on potato's germination",

}

Genetic Programming entries for Yu Yang Xin Zhao Min Huang Xin Wang2 Qibing Zhu

Citations