Abstract
The identification of germ integrity is very important for the study of germ rice grains. However, the traditional algorithms lack to achieve better recognition results. This article proposes a specific germ integrity detection model that improves the inception-resnet v1 model and adopts topological structure branch of inception-v4. The proposed model dilates the convolution based on the original model that gives an explicit description of image features for germ rice. The proposed scheme achieves the better approximation of abstract representations for potential space. Simultaneously, this paper is based on color image features for germ rice to identify its image location. Afterward, the rice germ gathered in the training will be unified its location by image rotation in accordance with the location information of germ. The proposed model is increasing the identification precision of rice germ integrity. It further strengthens the training of germ detail features. The experiment result shows that comprehensive recognition accuracy in this paper is up to 90.43%, compared with other traditional recognition methods, the algorithm accuracy has highly improved.
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This research was funded by basic scientific research business fees of central colleges and universities project, Grant Number HEUCFG201821.
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Conceptualization, B. L. and J. L.; methodology, S. L.; validation, S. L.; formal analysis, B. L.; investigation, S. L.; resources, B. L.; data curation, S. L.; writing—original draft preparation, B. L. and S. L.; writing—review and editing, B. L. and S. L. and J. L; visualization, S. L.; supervision, S. L.; project administration, B. L.; funding acquisition, B. L. All authors have read and agreed to the published version of the manuscript.
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Li, J., Li, S., Li, B. et al. Germ integrity detection for rice using a combination of germ color image features and deep learning. Soft Comput 26, 10717–10727 (2022). https://doi.org/10.1007/s00500-022-06902-6
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DOI: https://doi.org/10.1007/s00500-022-06902-6