{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:40:45Z","timestamp":1776444045856,"version":"3.51.2"},"reference-count":22,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2022,4,26]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like \u201cThreshold segmentation\u201d and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-06-2021-0101","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T12:08:34Z","timestamp":1637237314000},"page":"184-197","source":"Crossref","is-referenced-by-count":43,"title":["An IoT-based agriculture maintenance using pervasive computing with machine learning technique"],"prefix":"10.1108","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7212-078X","authenticated-orcid":false,"given":"Swathi","family":"Kailasam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9960-6373","authenticated-orcid":false,"given":"Sampath Dakshina Murthy","family":"Achanta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Rama Koteswara Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2611-4925","authenticated-orcid":false,"given":"Ramesh","family":"Vatambeti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9836-3683","authenticated-orcid":false,"given":"Saikumar","family":"Kayam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"issue":"1","key":"key2024071714074793800_ref001","first-page":"211","article-title":"Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features","volume":"15","year":"2013","journal-title":"Agricultural Engineering International: CIGR Journal"},{"key":"key2024071714074793800_ref002","first-page":"134","article-title":"An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases","year":"2011"},{"issue":"6","key":"key2024071714074793800_ref003","doi-asserted-by":"crossref","first-page":"31","DOI":"10.9790\/2834-0263134","article-title":"Remote area plant disease detection using image processing","volume":"2","year":"2012","journal-title":"IOSR Journal of Electronics and Communication Engineering"},{"key":"key2024071714074793800_ref004","doi-asserted-by":"crossref","unstructured":"Beucher, S. and Meyer, F. 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