[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122797-en":3,"doc-seo-122797-105":31,"detail-sidebar-cat-0-en-105":92},{"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},122797,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning for IoT-based smart farming - Journal of Advanced Zoology Article","Agriculture underpins food security and the supply of raw materials for many industries, while rapidly rising demands increase pressure on water and resource management. Smart farming improves crop growth analysis by monitoring key parameters and using machine learning to predict soil conditions, classify land types, and recommend suitable crops. A sensor-based IoT system measures temperature, pH, humidity, gas, and water level, transmits data via microprocessor to cloud, and supports timely decision-making through KNN predictive processing.","JOURNAL OF ADVANCED ZOOLOGY  \nISSN: 0253-7214  \nVolume 44 Special Issue-3 Year 2023 Page 1294:1298  \nMachine learning for IoT-based smart farming  \nMr. A.Ramesh Kumar  \nElectronics and Communication Engineering (Associate Professor) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India [rameshkumar_@vnrvjiet.in](rameshkumar_@vnrvjiet.in)  \nMs. K.B Archana  \nElectronics and Communication Engineering (Sr. Assistant Professor) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India [archana_k@vnrvjiet.in](archana_k@vnrvjiet.in)  \nP.Medhinya  \nElectronics and Communication Engineering  \nVNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India [20071D5509@vnrvjiet.in](20071D5509@vnrvjiet.in)  \n\n| Article History\u003Cbr>RECEIVED DATE : 01/07/2023\u003Cbr>REVISED DATE: 30/09/2023\u003Cbr>ACCEPTANCE DATE : 05/10/2023\u003Cbr>CCLicense\u003Cbr>CC-BY-NC-SA 4.0 | Abstract—Agriculture balances food requirements for mankind, and the supply of essential raw materials for many industries is the fundamental occupation in India. Smart farming allows analyzing the growth of crops and the parameters which influence crop growth and supports farmers in their activities, it is more profitable and reduces irrigation wastages. The proposed model is a smart farming system that analyzes the influence of parameters on crop growth and predicts the soil condition using a machine learning algorithm. Temperature, Ph, humidity, gas, and water level are the few most essential parameters to determine the quantity of water required and to find hazardous gas in any agriculture field. This system comprises temperature, pH, humidity, smoke detector, and water level sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. In this study, we present a model that predicts soil series with regard to land type and, in accordance with the prediction, suggests appropriate crops. For soil land classification and crop prediction application is developed using KNN algorithms. Three steps are necessary for its implementation: the first is data collecting using sensors placed in an agricultural field, the second is data cleaning and storage, and the third is predictive processing utilizing the ML technique. The results obtained through the algorithms are sent to the cloud, which helps in decision-making in advance.\u003Cbr>Keywords—Smart Farming, Precision agriculture, IoT, Machine Learning |\n| --- | --- |\n\n.  \nI. INTRODUCTION  \nAgriculture uses more than 85% of freshwater resources present on this planet and this percentage is gradually reducing because of population growth and an increase in food requirements. Therefore, there is a quick need for an increase in crop growth by developing new strategies based on science and technology. One of the most important factors for a crop's growth is the soil. Rainfall, temperature, soil type, fertilizers, and pH are all taken into account while making agricultural decisions. Crop cultivation is greatly influenced by the weather and the nutrients in the soil, which promote crop growth. For a crop to grow, soil is a key component. Nutrients found in soil are utilized by plants to grow. Various soil kinds are available, with each possessing various characteristics. The type of soil has a significant impact on the production of crops. We can increase production by selecting the proper crop for the correct sort of land. This can be accomplished by first studying the soil and then grouping it into several soil types. From previous decades, the success of theoretical research contribution,  \nnowadays the need of using IoT in agriculture applications becomes a reality. IoT with the adoption of information technology solutions in crop fields with the help of smart agriculture contributes to minimal usage of water, and protection of fields from dangerous gases, including technical agronomic, managerial, and so on. For achieving water saving, several rese","cbCailBaE8nJpzQv","https://ap.wps.com/l/cbCailBaE8nJpzQv","pdf",638384,4,1,5,"English","en",105,"# Introduction\n## IoT and precision agriculture background\n## Soil factors and crop growth decision support","[{\"question\":\"What does the proposed smart farming system predict and how is it used?\",\"answer\":\"The model predicts soil series/soil condition based on monitored land-related parameters, then suggests appropriate crops according to the prediction for better cultivation decisions.\"},{\"question\":\"Which sensors and parameters are used to collect data?\",\"answer\":\"Temperature, pH, humidity, gas/smoke, and water level are measured using sensors deployed in an agricultural field.\"},{\"question\":\"What are the main steps for implementing the KNN-based application?\",\"answer\":\"Implementation involves data collecting with field sensors, data cleaning and storage, and predictive processing using the machine learning technique, followed by sending results to the cloud for decision-making.\"}]","Machine learning for IoT-based smart farming - 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