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In this newly developed program, a machine learning algorithm has been modified and adapted to give the proportion of different colors present in a leaf. Python script is developed for an image processing. For validation, experiments are conducted in green house to grow Axonopus compressus. Script first extracts different RGB (Red Green and Blue) colors present in the leaf using the K-means clustering algorithm. Appropriate centroids required for the clusters of leaf colors are formed by the K-means algorithm. The new program provides saves computation time and gives output in form of different colors proportion as a CSV (Comma-Separated Values) file. This study is the first step towards the demonstration of using automated programs for the segregation of colors from the leaf in order to access the growth of the plant in an urban landscape.<\/jats:p>","DOI":"10.3233\/jifs-201542","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T20:13:56Z","timestamp":1606248836000},"page":"1219-1243","source":"Crossref","is-referenced-by-count":5,"title":["Exploring simple K-means clustering algorithm for automating segregation of colors in leaf of Axonopus compressus: Towards maintenance of an urban landscape"],"prefix":"10.1177","volume":"40","author":[{"given":"Lovepreet","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Shantou University, China"},{"name":"Department of Computer Science and Engineering, Indian Institute of Technology, Gandhinagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Shantou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanandam","family":"Bordoloi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, IIT Guwahati, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Garg","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Shantou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Architecture, Guangxi University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-201542_ref1","doi-asserted-by":"crossref","first-page":"126380","DOI":"10.1016\/j.ufug.2019.126380","article-title":"Growth dynamics of deciduous species during their life period: A case study of urban green space in India","volume":"43","author":"Gadi","year":"2019","journal-title":"Urban Forestry & Urban Greening"},{"issue":"6","key":"10.3233\/JIFS-201542_ref2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s11032-016-0509-4","article-title":"Genetics and fine mapping of a yellow-green leaf gene (ygl-1) in cabbage (Brassica oleracea var. capitata L.)","volume":"36","author":"Liu","year":"2016","journal-title":"Molecular Breeding"},{"key":"10.3233\/JIFS-201542_ref3","doi-asserted-by":"publisher","first-page":"105069","DOI":"10.1016\/j.compag.2019.105069","article-title":"Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping","volume":"167","author":"Zhang","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"issue":"5","key":"10.3233\/JIFS-201542_ref4","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/S1672-6308(13)60142-0","article-title":"Genetic analysis and molecular mapping of novel white striped leaf mutant gene in rice","volume":"20","author":"Sheng","year":"2013","journal-title":"Rice Science"},{"key":"10.3233\/JIFS-201542_ref5","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.compag.2018.10.006","article-title":"Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images","volume":"155","author":"Kerkech","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"issue":"5","key":"10.3233\/JIFS-201542_ref6","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s11119-016-9495-0","article-title":"Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions","volume":"18","author":"Chemura","year":"2017","journal-title":"Precision Agriculture"},{"key":"10.3233\/JIFS-201542_ref7","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.ecolind.2018.01.012","article-title":"Are remotely sensed traits suitable for ecological analysis? 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