[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118431-en":3,"doc-seo-118431-105":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},118431,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Intelligent Water Management in Precision Agriculture Using Machine Learning","India’s agriculture-driven economy faces rising pressure to increase crop quality and quantity while reducing water and other resource consumption. High investment costs for automated irrigation remain a major barrier for farmers. This study proposes a machine learning approach to optimize irrigation automation costs by reducing the deployment and operational burden of remote sensor networks. Using a dataset of soil moisture and temperature, k-means clustering supports learning, while two algorithms predict irrigation treatments, improving accuracy and training efficiency. Performance is cost-efficient, with future work focused on further prediction accuracy to ensure optimal operation.","Intelligent Water Management in Precision Agriculture Using Machine Learning  \nKhadri S S, and Dr.Arpana Bharani  \nDepartment of Computer Science, Dr. A. P. J. Abdul Kalam University, Indore (M.P.)  \nEmail [ID:khadri885@gmail.com](ID:khadri885@gmail.com), [arpanabharani@gmail.com](arpanabharani@gmail.com)  \nAbstract—India's economy heavily relies on agriculture, with a significant portion of the population engaged in agriculturebased businesses. Automation in agriculture holds the potential to enhance crop production quality and quantity while reducing resource consumption. However, the high investment costs associated with agricultural automation present a major challenge. To address this, a Machine Learning (ML) technique is proposed to optimize the costs associated with automating agricultural irrigation systems.The proposed ML model aims to reduce the implementation and operational costs of the remote sensor network used for irrigation. A dataset comprising soil moisture and temperature data was utilized, with the irrigation treatment type serving as the target variable. The dataset underwent preprocessing to ensure suitability for learning, followed by the application of k-means clustering for behavioral data analysis. This clustering technique grouped similar sensor readings, improving learning performance in terms of accuracy and training time.Two machine learning algorithms were then implemented to train the model and predict irrigation treatments, effectively minimizing the cost of deploying and maintaining sensors in the field. While the current system demonstrates significant cost efficiency, its overall performance remains reliant on the accuracy of the prediction model. Future work will focus on further enhancing the prediction accuracy to ensure optimal performance.This study highlights the potential of intelligent water management systems in precision agriculture, demonstrating how machine learning can contribute to sustainable and cost-effective agricultural practices.  \nKeywords—Machine learning, algorithm designs, support system, irrigation system, sensor network, prediction, automation.  \n1. INTRODUCTION  \nIndia is a predominantly agricultural country, with over 70% of its population relying on agriculture and related activities for their livelihood. However, a significant portion of farmers face challenges due to limited resources and expertise. These challenges include difficulties in monitoring, irrigation, fertilization, disease management, and achieving high crop yields. As a result, many farmers experience losses during every crop production cycle. Smart farming and precision farming have emerged as promising techniques to address these issues, but their implementation and maintenance are prohibitively expensive for most farmers. The costs of establishing sensor networks, communication channels, cloud servers, and employing monitoring technologies, along with their maintenance, require considerable investment and expertise.  \nTo address these challenges, this paper proposes a Machine Learning (ML) model aimed at reducing the cost of sensor  \nnetworks and automating irrigation systems. Automation in irrigation enhances crop productivity while conserving water and other resources. The proposed system consists of three key modules: data collection, data analysis and decisionmaking, and automated water supply. Unlike traditional irrigation methods, where farmers manually assess farmland  \nand decide on water supply, this system relies on a machine learning-based decision-making process. This approach eliminates the need for costly ground sensors by leveraging affordable data collection methods and intelligent decisionmaking.  \nThe paper begins with a review of recent advancements in irrigation system automation to identify essential datasets, machine learning techniques, and key features for consideration. The proposed model utilizes soil moisture and temperature data to train machine learning algorithms, ","cbCaifes3IQvQnC0","https://ap.wps.com/l/cbCaifes3IQvQnC0","pdf",548442,1,7,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"How does the proposed system reduce irrigation automation costs?\",\"answer\":\"It uses machine learning to minimize the implementation and operational costs of the remote sensor network used for irrigation, aiming to reduce the need for costly ground sensors through affordable data collection.\"},{\"question\":\"What data and techniques are used to train the prediction model?\",\"answer\":\"The model uses soil moisture and temperature data, applies preprocessing for suitability for learning, and employs k-means clustering to analyze behavioral patterns from sensor readings.\"},{\"question\":\"How are irrigation treatments predicted in the system?\",\"answer\":\"Two machine learning algorithms are trained to predict irrigation treatments based on clustered sensor readings, enabling more accurate decisions for automated water supply.\"}]","Intelligent Water Management in Precision Agriculture Using Machine Learning | PDF",1785683582,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"intelligent-water-management-in-precision-agriculture-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intelligent-water-management-in-precision-agriculture-using-machine-learning/118431/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed system reduce irrigation automation costs?","Question",{"text":75,"@type":76},"It uses machine learning to minimize the implementation and operational costs of the remote sensor network used for irrigation, aiming to reduce the need for costly ground sensors through affordable data collection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and techniques are used to train the prediction model?",{"text":80,"@type":76},"The model uses soil moisture and temperature data, applies preprocessing for suitability for learning, and employs k-means clustering to analyze behavioral patterns from sensor readings.",{"name":82,"@type":73,"acceptedAnswer":83},"How are irrigation treatments predicted in the system?",{"text":84,"@type":76},"Two machine learning algorithms are trained to predict irrigation treatments based on clustered sensor readings, enabling more accurate decisions for automated water supply.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]