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This study designs and preliminarily validates an IoT and machine learning smart irrigation framework at TRL3, combining real-time sensors, external weather data, and Random Forest forecasting to estimate crop water demand and support adaptive scheduling. Evaluations show strong predictive accuracy (RMSE 0.19 L/m², MAE 0.16 L/m²) and potential ~30% irrigation water savings versus fixed scheduling while maintaining adequate water availability, improving water-use efficiency and reducing operational costs through data-driven decision support.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Model for Water Optimization in Vegetable Gardens  \n1Novi Indah Pradasari, 2Eka Wahyudi, 3Rizqia Lestika Atimi,  \n4Ar-Razy Muhammad, 5Darmanto, 6Indra Pratiwi  \n1,2,3,4,5,6 Information Technology, Ketapang State Polytechnic, Ketapang  \n[1](1novi.ip@politap.ac.id)[novi.ip@politap.ac.id](1novi.ip@politap.ac.id), [2](2eka.wahyudi@politap.ac.id)[eka.wahyudi@politap.ac.id](2eka.wahyudi@politap.ac.id), [3](3rizqia.lestika@politap.ac.id)[rizqia.lestika@politap.ac.id](3rizqia.lestika@politap.ac.id), [4](4ar-razy.muhammad@politap.ac.id)[ar-razy.muhammad@politap.ac.id](4ar-razy.muhammad@politap.ac.id), [5](5darmanto@politap.ac.id)[darmanto@politap.ac.id](5darmanto@politap.ac.id) , [6](6indra.pratiwi@politap.ac.id)[indra.pratiwi@politap.ac.id](6indra.pratiwi@politap.ac.id)  \nAbstract—Efficient water management has become increasingly important in modern agriculture due to growing increasing demand for finite water supplies and the necessity of promoting sustainable farming practices. Traditional time-based irrigation approaches often result in inefficient water use and limited adaptability to dynamic environmental conditions. This study presents the design and preliminary validation of an Internet of Things (IoT)-and Machine Learning (ML)-based smart irrigation framework at Technology Readiness Level (TRL) 3. The proposed framework integrates real-time sensor measurements, external weather information, and a Random Forest–based forecasting algorithm to determine crop water demand support adaptive irrigation scheduling. Experimental and simulation-based evaluations demonstrated that the Random Forest model achieved satisfactory predictive performance, with an RMSE of 0.19 L/m² and an MAE of 0.16 L/m². Furthermore, the proposed framework showed the potential to reduce irrigation water consumption by approximately 30% compared with conventional fixed-schedule irrigation while maintaining adequate water availability for crop growth. The integration of multi-source environmental data and predictive analytics enabled more accurate irrigation decisions, contributing to improved water-use efficiency and reduced irrigation-related operational costs. These findings highlight integrating connected sensing systems with machine learning techniques can facilitate evidence-based irrigation management while promoting long-term agricultural sustainability.  \nKeywords—Efficient water management; Smart Irrigation; Internet of Things; Machine Learning; Random Forest  \nI. Introduction  \nSustainable utilization of agricultural water resources is increasingly important as farming systems face limited freshwater availability, unpredictable climatic conditions, and rising requirements for food production. Inefficient irrigation management may result in unnecessary water losses, lower agricultural yields, and higher expenditures associated with farm operations. Consequently, the development of intelligent irrigation systems has become essential to support sustainable agricultural production. The emergence of IoT-based sensing networks has made it possible to collect environmental information in real time, whereas ML algorithms provide analytical capabilities for generating intelligent irrigation recommendations from the acquired data. [1], [2] .  \nThe use of Machine Learning techniques in agricultural systems has shown considerable effectiveness in predicting crop water  \ndemand by analyzing environmental factors, including soil moisture content, air temperature, and relative humidity. Intelligent learning algorithms support dynamic irrigation management by adjusting water delivery according to environmental fluctuations, thereby reducing water wastage while sustaining agricultural output. [3], [4] . In parallel, IoT technologies have been increasingly adopted in precision agriculture to support automated monitoring and control through continuous environmental data collection [5], [6] . The convergence of IoT and ML t","cbCaiaDThLHXeSaH","https://ap.wps.com/l/cbCaiaDThLHXeSaH","pdf",485270,"English","# Introduction\n## Background and motivation\n## Limitations of existing smart irrigation systems\n## Proposed IoT and ML smart irrigation framework","[{\"question\":\"What problem does the smart watering model address?\",\"answer\":\"It addresses inefficient water use in irrigation caused by limited adaptability of time-based approaches under dynamic environmental conditions.\"},{\"question\":\"How does the proposed framework estimate crop water demand?\",\"answer\":\"It integrates real-time sensor measurements and external weather information with a Random Forest forecasting algorithm to determine crop water demand and enable adaptive irrigation scheduling.\"},{\"question\":\"What performance results were reported for the Random Forest model?\",\"answer\":\"The reported predictive performance includes RMSE of 0.19 L/m² and MAE of 0.16 L/m².\"}]","IoT and Machine Learning-Based Smart Watering Model - Water Optimization in Vegetable Gardens | PDF"]