{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:40Z","timestamp":1777704880907,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,1,4]]},"abstract":"<jats:p>Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a \u2018simple-squash\u2019 in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets.<\/jats:p>","DOI":"10.3233\/jifs-201226","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T11:47:25Z","timestamp":1604663245000},"page":"1025-1036","source":"Crossref","is-referenced-by-count":6,"title":["Capsule network with K-Means routingfor plant disease recognition"],"prefix":"10.1177","volume":"40","author":[{"given":"Patrick Kwabena","family":"Mensah","sequence":"first","affiliation":[{"name":"Department of Computer Science & Informatics, University of Energy and Natural Resources, Sunyani, Ghana"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin Asubam","family":"Weyori","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Informatics, University of Energy and Natural Resources, Sunyani, Ghana"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mighty Abra","family":"Ayidzoe","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-201226_ref1","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1146\/annurev.phyto.37.1.399","article-title":"Climate Change and Plant Disease Management","volume":"37","author":"Coakley","year":"1999","journal-title":"Annu Rev Phytopathol"},{"key":"10.3233\/JIFS-201226_ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.3233\/JIFS-201226_ref3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-981-32-9298-7_4","article-title":"Image Recognition of Peanut Leaf Diseases Based on Capsule Networks, in","author":"Dong","year":"2019","journal-title":"ICAI 2019"},{"key":"10.3233\/JIFS-201226_ref4","first-page":"3857","article-title":"Dynamic routing between capsules, in -Decem, no. 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