[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126446-en":3,"doc-seo-126446-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126446,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Comparative analysis of machine learning models for smart irrigation systems","Intelligent irrigation systems address water scarcity, climate variability, and the need for sustainable agricultural production. They estimate both the efficient timing and exact irrigation quantity using data-driven methods, aiming for maximum crop yield with reduced water use. This study compares four widely used machine learning models—SVM, Gradient Boosting, KNN, and Logistic Regression—to predict irrigation requirements from environmental and agronomic variables. Sensor-network data include soil moisture, air temperature, humidity, solar radiation, and crop type, evaluated with accuracy, precision, recall, F1 score, and AUC.","Comparative analysis of machine learning models for smart  \nirrigation systems  \nYusuf Owolabi Olatunde1*, Oluwafolake Esther Ojo1, Oluwatobi Adedamola AyilaraAdewale 1, Glorious Omokunmi Anjorin-Adeboye2, Taiwo Samson Olutoberu3 1Osun State University, Osogbo, Nigeria 2Bells University of Technology, Ota, Nigeria  \n3Federal University of Agriculture, Abeokuta, Nigeria  \n*[Corresponding author: yusuf.olatunde@uniosun.edu.ng](Corresponding author: yusuf.olatunde@uniosun.edu.ng)  \n\n| ARTICLE INFO | ABSTRACT |\n| --- | --- |\n| DOI:10 .46223/HCMCOUJS. | Intelligent irrigation systems play a crucial role in |\n| tech.en.15.2.4520.2025 | addressing the global issues of water scarcity, climate variability, and sustainable agricultural production. These systems can help identify the efficient time and the exact quantity of irrigation through the use of data-driven ideas, which ensures maximum crop yield with minimal use of water. This paper provides a thorough comparative analysis of the four most commonly used Machine Learning (ML) models: Support Vector Machines (SVM), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), to predict the need of irrigation |\n| Received: June 26th, 2025\u003Cbr>Revised: July 13th, 2025 | based on critical environmental and agronomic variables. The dataset features include soil moisture, air temperature, relative humidity, solar radiation, and crop types, among other features, |\n| Accepted: July 29th, 2025 | obtained using sensor networks installed on farmland. We trained and tested each model before comparing its performance using standard evaluation metrics, which include accuracy, precision, recall, F1 Score, and the Area Under the Curve. These findings indicate that GB and KNN models performed better than SVMand LR. For instance, GB and KNN achieved precisions of 95.6% and 92.4%, respectively, compared to SVM and LR, which achieved precisions of 86.2% and 72.8%, respectively. In both accuracy and generalization, the GB model performs overall best. This study contributes a fair investigation of the suitability of |\n| Keywords: | well-known ML models in irrigation forecasting for smart |\n| irrigation prediction; machine learning models; precision agriculture; smart irrigation; water resource management | farming in the south-western region of Nigeria. This study makes use of a region-specific dataset that is gathered by sensor networks, involving 100,000 records in two farming seasons. |\n\n1. Introduction  \nOne of the most critical issues that the 21st-century agricultural sector of the 21st century will have to address is the efficient management of water. In the face of growing pressure from climate change, urbanization, and population growth, the necessity of making sustainable food production and the preservation of water resources a global priority should be  \nacknowledged. Approximately 70% of the world’s water used in agriculture is lost due to poor irrigation practices (Dotaniya et al., 2023; Shemer et al., 2023) . Even the conveyor irrigation systems tend to be based on set schedules or the subjective judgment of the farmer, resulting in either over- or under-irrigation. Not only do such inefficiencies result in water wastage, but crops and soil are also likely to suffer, and agricultural productivity in the long term cannot help but be adversely affected.  \nTo overcome these challenges, the idea of intelligent irrigation systems has been presented as an innovative service, which helps to get accurate and real-time irrigation advice by using sensors (Devadiga et al., 2024), the Internet of Things (IoT) (Srikanthnaik, 2024), and artificial intelligence (Younes et al., 2024) in the process. Of them, Machine Learning (ML) models have emerged as a promising methodology of predicting irrigation requirements based on the dynamic variables of the environment and crop-related parameters, including soil moisture, air temperature, humidity, solar radiation, and crop type, among othe","cbCaipV0kAyHGljm","https://ap.wps.com/l/cbCaipV0kAyHGljm","pdf",732198,5,1,14,"English","en",105,"# Introduction\n# Related work","[{\"question\":\"Which machine learning models are compared for smart irrigation prediction?\",\"answer\":\"The study compares Support Vector Machines (SVM), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Logistic Regression (LR) for irrigation requirement prediction.\"},{\"question\":\"What inputs does the dataset include for the irrigation forecasting task?\",\"answer\":\"The dataset uses sensor-network-derived features such as soil moisture, air temperature, relative humidity, solar radiation, and crop types.\"},{\"question\":\"How are the models evaluated and what metric results are highlighted?\",\"answer\":\"Models are assessed using accuracy, precision, recall, F1 score, and AUC; the findings indicate GB and KNN outperform SVM and LR, with GB achieving the best overall performance in accuracy and generalization.\"}]","Comparative analysis of machine learning models for smart irrigation systems | PDF",1785905097,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparative-analysis-of-machine-learning-models-for-smart-irrigation-systems","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-models-for-smart-irrigation-systems/126446/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning models are compared for smart irrigation prediction?","Question",{"text":77,"@type":78},"The study compares Support Vector Machines (SVM), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Logistic Regression (LR) for irrigation requirement prediction.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What inputs does the dataset include for the irrigation forecasting task?",{"text":82,"@type":78},"The dataset uses sensor-network-derived features such as soil moisture, air temperature, relative humidity, solar radiation, and crop types.",{"name":84,"@type":75,"acceptedAnswer":85},"How are the models evaluated and what metric results are highlighted?",{"text":86,"@type":78},"Models are assessed using accuracy, precision, recall, F1 score, and AUC; 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