[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120944-en":3,"doc-seo-120944-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120944,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","IoT and Machine Learning-Based Prediction of Smart Soil Moisture Monitoring and Irrigation System - presentation","A country like India faces an acute water shortage, with 35 million people lacking access to safe water, while groundwater extraction remains central to irrigation and drinking supplies. The work proposes an IoT-enabled approach combined with machine learning to estimate soil moisture from sensor-collected temperature and humidity data. Data from agricultural sensor nodes is transmitted via IoT and stored in a cloud database, then used for classification with Naive Bayes, Logistic Regression, and Support Vector Machine. Reported accuracies reach about 98.8–99.3%.","IoT and Machine Learning-Based Prediction of Smart Soil Moisture Monitoring and Irrigation  \nSystem  \nDeepak Yadav  \nDepartment of Computer Science & Engineering,VBS Purvanchal University  \nJaunpur (U.P.)-222002  \n[Deepak.engg84@gmail.com](Deepak.engg84@gmail.com)  \nSom Pal Gangwar  \nDepartment of Electronics Engineering, Kamla Nehru Institute of Technology  \nSultanpur (U.P.)-228118  \n[gangwarsp.gangwar@gmail.com](gangwarsp.gangwar@gmail.com)  \nGitanjali Verma  \nDepartment of Computer Engineering & Applications, GLA University  \nMathura (U.P.)-281406  \n[gitanjai687@gmail.com](gitanjai687@gmail.com)  \nAdesh Kumar Mishra  \nDepartment of Electrical Engineering, Babu Banarasi das Institute of Technology & Management,  \nLucknow (U.P)-226028  \n[adesh143mishra@gmail.com](adesh143mishra@gmail.com)  \nRajeev Kumar Srivastava  \nNirmala Devi Polytechnic College,  \nJaunpur (U.P.)-222133  \n[raj.k.sri@gmail.com](raj.k.sri@gmail.com)  \nSanjeev Kumar Srivastava,  \nDepartment of Environmental Sciences, Dr. Rammanohar Lohia Avadh University,  \nAyodhya (U.P.)-224001  \n[sanjsri2001@gmail.com](sanjsri2001@gmail.com)  \nMahima Chaurasia  \nDepartment of Environmental Sciences, Dr. Rammanohar Lohia Avadh University  \nAyodhya (U.P.)-224001  \n[mahimaenv@gmail.com](mahimaenv@gmail.com)  \nAnkit Kumar Srivastava  \nDepartment of Electrical Engineering , Dr. Rammanohar Lohia Avadh University  \nAyodhya (U.P.)-224001,  \n[ankitsrivastava@rmlau.ac.in](ankitsrivastava@rmlau.ac.in)  \nAbstract—A country like India faces an acute water shortage, with 35 million people lacking access to safe water. India is the world's largest groundwater user, as tube wells, the main source of irrigation for Indians, provide 46% of water for irrigation. IoT and machine learning can be vital in overcoming acute water shortages and achieving optimum water resource utilization. This paper aims to present an ML model to estimate the soil moisture level and IoT to act upon it. We are introducing a working plan to collect data on soil moisture, temperature, and humidity, utilizing sensor nodes deployed in the agricultural field to gather various sensor data. The gathered data is forwarded through IoT  \nand stored in a cloud-based database like MongoDB. This data applies to machine learning techniques for classification. Several models, such as Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine models (SVM), are utilized. The experimental results, with accuracy rates of 98.8%, 99.0%, and 99.3% for Naive Bayes, Logistic Regression, and Support Vector Machine models respectively. The combination of IoT and machine learning helps to achieve environmental goals efficiently in water resource utilization and better crop yield.  \nKeywords-Machine learning; IoT; Sensor nodes; SVM; NB; LR  \nI. INTRODUCTION  \nAgriculture, as it is practiced today in most parts of the world, is facing many environmental and climatic challenges, including over-exploitation of irrigation resources and shrinking land resources. Sustainable farming techniques are crucial for ensuring the achievement of the  \nSustainable Development Goals (SDGs) . Precision farming practices enhance sustainability by using data-driven insights. Precision agriculture minimizes environmental impact, making it a valuable tool for sustainable and efficient farming. Metrological data play an important role in the estimation of surface moisture, crop yield, or forecasting weather information, disease, and insect outbreaks[1] . The continuous change in climatic conditions has compelled researchers to gather weather data, facilitating a deeper analysis that offers enhanced insights into the agriculture industry[2] .  \nComputer vision is pivotal in various aspects of agriculture, particularly in product quality assessment, crop monitoring, and automation. One of its most prominent applications is in the automated detection of diseases in crops[3] . By integrating computer vision with artificial intelligence (AI) and deep learnin","cbCaipQMc1cVtcxp","https://ap.wps.com/l/cbCaipQMc1cVtcxp","pdf",794608,1,"English","en",105,"# Introduction\n## Precision farming and environmental challenges\n## Role of moisture monitoring and irrigation control\n# Proposed smart irrigation approach\n## Data collection with IoT sensor nodes\n## Cloud storage and machine learning classification\n# Experimental results\n## Model accuracies and comparison","[{\"question\":\"What problem does the proposed system address?\",\"answer\":\"It targets acute water shortage and inefficient groundwater use by predicting soil moisture to support better irrigation scheduling and water management.\"},{\"question\":\"How is data collected and processed in the system?\",\"answer\":\"Soil moisture, temperature, and humidity are gathered using sensor nodes in agricultural fields, transmitted through IoT, and stored in a cloud-based database before applying machine learning.\"},{\"question\":\"Which machine learning models are used and how well do they perform?\",\"answer\":\"Naive Bayes, Logistic Regression, and Support Vector Machine are used for classification, achieving accuracy rates of approximately 98.8%, 99.0%, and 99.3% respectively.\"}]","IoT and Machine Learning-Based Prediction of Smart Soil Moisture Monitoring and Irrigation System - 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