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In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and\n                    <jats:italic>\u03b2<\/jats:italic>\n                    . The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate.\n                  <\/jats:p>","DOI":"10.3233\/jifs-182893","type":"journal-article","created":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T12:43:56Z","timestamp":1561466636000},"page":"1499-1511","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["3DPCNN based on whale optimization algorithm for color image segmentation"],"prefix":"10.1177","volume":"37","author":[{"given":"Zhikai","family":"Xing","sequence":"first","affiliation":[{"name":"Northeast Forestry University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heming","family":"Jia","sequence":"additional","affiliation":[{"name":"Northeast Forestry University, Harbin, 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