[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126688-en":3,"doc-seo-126688-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126688,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","An Efficient Hydro-crop Growth Prediction System for Nutrient Analysis Using Machine Learning Algorithm","An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm is proposed to improve hydro nutrient management for crop yield. The approach designs a hydro-crop growth prediction system that predicts crop yield rate by analyzing correlations between inputs—nutrient index, electric conductivity limit, ion concentration factors, and crop dry weight—and outputs such as crop yield rate. The system improves prediction accuracy and efficiency through optimal utilization of variables, supporting better-quality yield with smart forecasting.","An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm  \nChandana Chikkasiddaiah1,2, Parthasarathy Govindaswamy2, Mallikarjunaswamy Srikantaswamy3  \n1Master of Computer Applications, JSS Academy of Technical Education, Bengaluru, India  \n2School of Computing and Information Technology, REVA University, Bengaluru, India 3Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India  \nArticle history:  \nReceived Jan 28, 2023 Revised Mar 12, 2023 Accepted Apr 7, 2023  \nKeywords:  \nCrop yield rate Dry weight  \nElectric conductivity limit Hydro nutrient management Nutrient index  \nCorresponding Author:  \nThe hydro nutrient management (HNM) for crop yield is effectively improved using proposed system. A hydro-crop growth prediction system (HCGPS) is designed using machine learning. The reconfigurable nutrients uptake crop yield prediction rate is enhanced. This proposed HCGPS is used to predict the crop yield by considering input parameters such as nutrient index (NI), electric conductivity limit (ECL), ion concentration factors (ICF) and dry weight of the crop and crop yield rate (CYR) to analyze the positive and negative correlation with crop growth. The proposed system is used to find correlation Index of input and output parameters to determine the prediction rate of crop yield. The proposed design improves smart prediction rate and efficiency of crop growth rate with optimal utilization of input variables. This proposed HCGPS is very helpful to achieve good quality yield with optimal utilization of input parameters.  \nThis is an open access article under the CC BY-SA license.  \nChandana Chikkasiddaiah  \nMaster of Computer Applications, JSS Academy of Technical Education Bengaluru, Karnataka, India  \n[Email: punyachandu@gmail.com](Email: punyachandu@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nHydro nutrient management is a soilless technique to nurture yields in water. Such zero-soil farming way characterizes kind of an outstanding chance for the cultivation sphere, particularly in fields connecting with trials like uncontrollable soil deprivation and controlled water bases. Additionally, such farming practice proves outstanding consequences concerning an atmosphere and user responsive agriculture. It is similarly a consistent tool for the forthcoming trials in food security. Numerous areas of the world which face trials like volatile weather forms, extreme heat, condensed space obtainability, are captivating hydro nutrient management (HNM) as a substitute method to agriculture and a probable answer for such difficulties. The global marketable HNM business has increased four-five-fold in the preceding 10 years and is presently estimated amid 21k and 26k hectares with a farm gate value of US $5.9 to $7.9 billion. As a consequence, agriculture perceives are quickly shifting to smarter and accuracy agriculture observes for enhanced crop harvests and commercial gains. With the novel age of big data, approaches like machine learning (ML) and artificial intelligence (AI) available, data can be used to analyze, yield patterns and make forecasts [1], [2] .  \nDepending on the above concerns investigators have advised and exasperated to overcome such breaches by uniting machine HNM agriculture can be mainly estranged to 3 types: nutrient film technique (NFT), Aquaponics and Deep Flow method. TheNFT is 1 of the hydroponic ways such that a thin torrent of water having the compulsory dissolved nutrients otherwise known as NS is used that is an upright standard for development of the plant. This one is re-disseminated in the plants roots in the pipelines that are watertight, furthermore called  \notherwise as channels. In this scheme, NS is augmented with substances such as rock wool, sand that is conceded and re-disseminated inside a slope containing of plants positioned in a plastic furrow [3], [4] .  \nThis delivers the finest quantity of nutrients ","cbCaiiuKfugv2ceR","https://ap.wps.com/l/cbCaiiuKfugv2ceR","pdf",593432,1,10,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"What does the proposed hydro-crop growth prediction system do?\",\"answer\":\"It predicts crop yield rate using machine learning by analyzing relationships between nutrient-related inputs and crop growth outcomes.\"},{\"question\":\"Which input parameters are used to make the predictions?\",\"answer\":\"The system uses nutrient index, electric conductivity limit, ion concentration factors, and the crop’s dry weight, along with crop yield rate for correlation analysis.\"},{\"question\":\"How does the system improve prediction effectiveness?\",\"answer\":\"It finds correlations among input and output parameters to enhance the smart prediction rate and efficiency while using input variables optimally.\"}]","An Efficient Hydro-crop Growth Prediction System for Nutrient Analysis Using Machine Learning Algorithm | 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does the proposed hydro-crop growth prediction system do?","Question",{"text":75,"@type":76},"It predicts crop yield rate using machine learning by analyzing relationships between nutrient-related inputs and crop growth outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which input parameters are used to make the predictions?",{"text":80,"@type":76},"The system uses nutrient index, electric conductivity limit, ion concentration factors, and the crop’s dry weight, along with crop yield rate for correlation analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system improve prediction effectiveness?",{"text":84,"@type":76},"It finds correlations among input and output parameters to enhance the smart prediction rate and efficiency while using input variables 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