Abstract
A novel method based on the regional feature of mature citrus is proposed for segmentation and recognition in order to achieve the segmentation and identification of mature citrus and branches-leaves in Harvesting robots. Feature mapping table is used to reduce the dimension of the feature vector. The ROI (region of interest) size of the target object is determined by the size of picking up the working space of the manipulator, the binocular camera field and the citrus. According to the scores, the consistency of the primary ROI is sorted to a greater degree, and the ROI with the largest score is chosen as the optimal area. The experimental results show that the accuracy of recognition is 94% and the time required for single image segmentation is 0.24 s. This method is better than many existing methods.
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References
Yang, S.: China’s citrus industry status and development trend. Agric. Eng. Technol. (Agro-Ind.) 04, 13–17 (2014)
Yang, L.: Research on target recognition and positioning technology of citrus picking robot based on binocular vision. Chongqing University of Science and Technology (2017)
Xiaojun, Z.: Mathematical picking robot positioning and obstacle detection. Jiangsu University (2009)
Jianrong, C., Xiaojun, Z., Feng, W., Qiang, L.: Obstacle identification technology of citrus picking robot. Trans. Agric. Mach. 40(11), 171–175 (2009)
Juntao, X., Xiangjun, Z., Hongjun, W., et al.: Realization of ripe litchi under different light conditions based on Retinex image enhancement. J. Agric. Eng. China 29(12), 170–178 (2013)
Hongxing, P., Xiangjun, Z., Lijuan, C., et al.: Fast identification of field lychee multicolor targets based on double Otsu algorithm. J. Agric. Mechanization 45(4), 61–68 (2014)
Okamoto, H., Lee, W.S.: Green citrus detection using hyperspectral imaging. Comput. Electron. Agric. 66(2), 201–208 (2009)
Stajnko, D., Lakota, M., Hočevar, M.: Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Comput. Electron. Agric. 42(1), 31–42 (2004)
Amatya, S., et al.: Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting. Biosyst. Eng. 146(Suppl. C), 3–15 (2016)
Bac, C.W., Hemming, J., van Henten, E.J.: Robust pixel-based classification of obstacles for robotic harvesting of sweet-pepper. Comput. Electron. Agric. 96, 148–162 (2013)
Juntao, X., Xiangjun, Z., Lijuan, C., Aixia, G.: Identification of ripe litchi in natural environment based on machine vision. Acta Autom. Mach. 42(09), 162–166 (2011)
Shun, F.: Design and research of end effector of citrus picking robot. Chongqing University of Science and Technology (2017)
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Liu, S., Yang, C., Hu, Y., Huang, L., Xiong, L. (2018). A Method for Segmentation and Recognition of Mature Citrus and Branches-Leaves Based on Regional Features. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_29
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DOI: https://doi.org/10.1007/978-981-13-1702-6_29
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