[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124507-en":3,"doc-seo-124507-105":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":20,"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},124507,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","A Predictive Model For Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data","This study develops and evaluates a machine learning model to predict optimal crop irrigation schedules using real-time environmental data from an IoT system. Building on a previously validated smart farming monitoring platform, the approach advances from reactive observation to proactive prediction. Sensor data collected over six days from DHT11 temperature/humidity modules and soil moisture sensors trains and validates an LSTM model that forecasts future soil moisture levels. Results show strong accuracy with MAE of 2.5%, RMSE of 3.1%, and R-squared of 0.92, supporting water savings and improved crop health.","A Predictive Model For Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data  \nRizki Agam Syahputra 1*, Dewi Andriani 2**  \n* Industrial Engineering Department, Universitas Teuku Umar  \n** Agrotechnology Department, Universitas Teuku Umar  \n[rizkiagamsyahputra@utu.ac.id](rizkiagamsyahputra@utu.ac.id1)[1](rizkiagamsyahputra@utu.ac.id1), [dewiandriani@utu.ac.id](dewiandriani@utu.ac.id2)[2](dewiandriani@utu.ac.id2)  \nArticle history:  \nReceived 2025-07-12 Revised 2025-08-01 Accepted 2025-08-10  \nKeyword:  \nIoT,  \nMachine Learning, Smart Farming.  \nThis study develops and evaluates a machine learning model for predicting optimal irrigation schedules using real-time environmental data collected from an Internet of Things (IoT) system. Building upon a previously validated smart farming monitoring system that provided real-time data on temperature, humidity, and soil moisture, this research addresses the next step: moving from monitoring to predictive analytics. Data collected over a six-day period from DHT11 temperature and humidity sensors, as well as soil moisture sensors, were used to train a predictive model. The model is designed to forecast future soil moisture levels, thereby providing farmers with proactive recommendations for irrigation. A Long Short-Term Memory (LSTM) neural network was employed to capture the temporal dependencies between atmospheric conditions and soil moisture. The model was trained on a portion of the collected data and then validated on a separate, unseen dataset. The evaluation yielded a Mean Absolute Error (MAE) of 2.5%, a Root Mean Square Error (RMSE) of 3. 1%, and an R-squared (R2) value of 0.92, demonstrating high predictive accuracy. This approach aims to enhance water resource management, reduce manual intervention, and improve crop health by ensuring water is supplied only when necessary. The results indicate that the machine learning model can accurately predict irrigation needs, offering a significant improvement over traditional, reactive monitoring systems and marking a substantial step towards data-driven, precision agriculture.  \nThis is an open access article under the CC–BY-SA license.  \nArticle Info ABSTRACT  \nI. INTRODUCTION  \nThe agricultural sector serves as the backbone of global food security and economic stability, a reality that holds particular significance for developing nations like Indonesia. Within Indonesia, the province of Aceh exemplifies the sector's importance, contributing a substantial 28.5% to the region's overall Gross Regional Domestic Product (GRDP)  \n[1], [2] . Despite this vital economic role, a significant portion of Aceh's agriculture remains entrenched in conventional and traditional methods. These long-standing practices, while culturally significant, are increasingly proving insufficient to overcome the complex challenges of modern agriculture, including the escalating impacts of climate change,  \npronounced resource limitations, and a growing consumer demand for higher-quality produce.  \nTraditional farming techniques, which often depend on generational knowledge, intuition, and intensive manual labor, lack the efficiency and precision needed to optimize resource allocation and maximize yields in today's demanding environment [3]. This approach frequently leads to significant inefficiencies, such as the over-application of water andagrochemicals. Such practices not only deplete vital resources like water but also contribute to environmental degradation, including soil health deterioration and water source pollution. The urgent need to modernize the agricultural sector is driven by this imperative to produce more food with fewer resources, sustainably feeding a growing population while safeguarding the environment for future generations [4] .  \nIn response to these challenges, Precision Agriculture has emerged as a transformative paradigm. At its core, precision agriculture leverages advanced technology to collect and ana","cbCaioW721YuvUcd","https://ap.wps.com/l/cbCaioW721YuvUcd","pdf",840956,1,9,"English","en",105,"# Introduction\n## Smart farming and precision agriculture context\n## From monitoring to predictive irrigation\n# Methodology\n## IoT data collection and sensors\n## LSTM model training and validation\n# Results and Evaluation\n## Error metrics (MAE, RMSE, R2)\n# Impact\n## Water management and reduced manual intervention","[{\"question\":\"What problem does the study address in crop irrigation?\",\"answer\":\"The study targets the gap between real-time monitoring and actionable irrigation decisions by predicting optimal irrigation schedules instead of requiring constant manual interpretation.\"},{\"question\":\"Which data sources are used to train the predictive model?\",\"answer\":\"Training uses six days of IoT-generated environmental data, including DHT11 temperature and humidity readings and soil moisture sensor measurements.\"},{\"question\":\"Why is an LSTM neural network used in this work?\",\"answer\":\"An LSTM model is employed to capture temporal dependencies between atmospheric conditions and soil moisture, enabling forecasting of future soil moisture levels for irrigation planning.\"}]","A Predictive Model For Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data | 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