Assessing the Efficacy of IoT-driven Machine Learning Models in Enhancing Chili Crop Growth and Yield Quality

Assessing the Efficacy of IoT-driven Machine Learning Models in Enhancing Chili Crop Growth and Yield Quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Efficacy of IoT-driven Machine Learning Models in Enhancing Chili Crop Growth and Yield Quality Subrahmanyam Kodukula, P. Vidyullatha, G S Pradeep Ghantasala, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4808129/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract This study analyses the plant growth and yield parameters in drip-irrigated and furrow irrigated chili fields in the Guntur region during Kharif 2021. As a part of the DST-SEED project, 48 farmers' chili crops were selected amongst 21 clusters to collect data on soil characteristics, fruits of each plant, their height, fruit length, and fruit yield. The present comparative study purposes to establish the growth and quality of chili fields in the selected drip-irrigated areas. The study's findings show that the approach of drip irrigation integrated with the Internet of Things (IoT) devices excelled in all parameters studied over the furrow irrigation method. The maximum and minimum plant heights in a drip-irrigated farmer's field were 102.3 and 77.46 cm, respectively. While furrow irrigated, farmer's fields produced plant heights of 93.3 and 70.3 cm, respectively. The maximum and minimum number of plant-1 fruits and fruit lengths are 110 and 81.5, 8.1, and 5.9 cm were recorded in the drip-irrigated farmer's field integrated with IoT devices. IoT devices were placed to control the water flow smartly through a mobile app. At the same time, furrow-irrigated farmer's fields could produce the lowest yield of fruits in plant-1; the fruit lengths (cm) are 94.57 and 70.21, 6.7 and 4.7 cm, respectively. The current research recommends that agricultural communities use drip irrigation integrated with IoT devices instead of the old conventional flooding techniques, assess the nutrient state of their soil, implement the indicated logical nutrient management practices, smart motor control, and plant high-yielding varieties or hybrids. This research underscores the importance of adopting modern agricultural techniques, including drip irrigation integrated with IoT devices and machine learning-based predictive models, for enhancing chili yield growth. Chili Capsicum annuum furrow irrigation drip irrigation IoT devices Machine Learning algorithms Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 02 Aug, 2024 Submission checks completed at journal 02 Aug, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4808129","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335395837,"identity":"0deed117-fb2f-4d76-8875-1c56ea84c4bf","order_by":0,"name":"Subrahmanyam Kodukula","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Subrahmanyam","middleName":"","lastName":"Kodukula","suffix":""},{"id":335395838,"identity":"bf7daf52-127c-49b0-a5c5-672fd80b7030","order_by":1,"name":"P. 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As a part of the DST-SEED project, 48 farmers' chili crops were selected amongst 21 clusters to collect data on soil characteristics, fruits of each plant, their height, fruit length, and fruit yield. The present comparative study purposes to establish the growth and quality of chili fields in the selected drip-irrigated areas. The study's findings show that the approach of drip irrigation integrated with the Internet of Things (IoT) devices excelled in all parameters studied over the furrow irrigation method. The maximum and minimum plant heights in a drip-irrigated farmer's field were 102.3 and 77.46 cm, respectively. While furrow irrigated, farmer's fields produced plant heights of 93.3 and 70.3 cm, respectively. The maximum and minimum number of plant-1 fruits and fruit lengths are 110 and 81.5, 8.1, and 5.9 cm were recorded in the drip-irrigated farmer's field integrated with IoT devices. IoT devices were placed to control the water flow smartly through a mobile app. At the same time, furrow-irrigated farmer's fields could produce the lowest yield of fruits in plant-1; the fruit lengths (cm) are 94.57 and 70.21, 6.7 and 4.7 cm, respectively. The current research recommends that agricultural communities use drip irrigation integrated with IoT devices instead of the old conventional flooding techniques, assess the nutrient state of their soil, implement the indicated logical nutrient management practices, smart motor control, and plant high-yielding varieties or hybrids. This research underscores the importance of adopting modern agricultural techniques, including drip irrigation integrated with IoT devices and machine learning-based predictive models, for enhancing chili yield growth.\u003c/p\u003e","manuscriptTitle":"Assessing the Efficacy of IoT-driven Machine Learning Models in Enhancing Chili Crop Growth and Yield Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-29 15:24:08","doi":"10.21203/rs.3.rs-4808129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-08-02T21:38:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-02T15:54:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2024-07-26T12:39:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"95e76808-f39a-498a-a2eb-f8963d64c713","owner":[],"postedDate":"August 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-29T15:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-29 15:24:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4808129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4808129","identity":"rs-4808129","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}