[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123209-en":3,"doc-seo-123209-105":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":4,"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},123209,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",6,"Technology","Smart irrigation with crop recommendation using machine learning approach","Smart irrigation with crop recommendation system supports farmers in increasing crop yield through sustainable resource management. The approach measures soil dielectric permittivity using gypsum block soil sensors and evaluates water potential along with nutrients including potassium, nitrogen, phosphorus, and soil pH to estimate available soil conditions. Sensor-derived quality data feeds machine learning models to recommend suitable crops for specific demography and environment. For farmer-friendly decision support, the system links the recommendation model to Telegram notifications.","Smart irrigation with crop recommendation using machine  \nlearning approach  \nAnitha Palakshappa1, Sowmya Kyathanahalli Nanjappa2, Punitha Mahadevappa2, Sinchana2  \n1Department of ISE, Ramaiah Institute of Technology, Visvesvaraya Technological University, Karnataka, India 2Department of ISE, JSS Academy of Technical Education, Visvesvaraya Technological University, Karnataka, India  \n\n| Article history:\u003Cbr>Received Feb 28, 2023 Revised Nov 11, 2023 Accepted Dec 6, 2023 | Increasing crop yield with sustainable growth is the primary requirement for farmers with a growing population. Effective management and conservation of depleting natural resources is a priority task. Decrease in manpower due to migrating population has forced automation in agriculture. In this work, an automatic water irrigation and an effective crop recommendation system is proposed. Gypsum blocks based soil sensor is used to measure dielectric permittivity associated with the tested soil. The water-potential present in soil, along with potassium (K), nitrogen (N), phosphorus (P), potential of hydrogen (pH) helps to quantify the soil nutrients available and the suitable crop that can be considered for harvesting in a specified demography and environment. Sensory data indicating soil quality obtained is used to recommend crops by utilizing machine learning approaches. Telegram application is linked to the recommendation model to assist decision making and to ensure farmer-friendliness by sending notifications periodically.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Automated agriculture Machine learning Random forest Smart agriculture\u003Cbr>Smart irrigation Soil prediction |  |\n\nCorresponding Author:  \nAnitha Palakshappa  \nDepartment of ISE, Ramaiah Institute of Technology, Visvesvaraya Technological University Belgaum-590018, Karnataka, India  \nEmail: [anitha.palakshappa@gmail.com](anitha.palakshappa@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCrops are the lifeline for a growing population and for the economy of any country across the globe, agriculture being its pillar. Agriculture uses 70% of the accessible fresh water globally. The quality of any crop depends on the water the farmer irrigates at the right time. Crops should not be damaged due to overwatering or underwater, and there should be a need to conduct regular checks at regular intervals. Water utilization can be optimized with the help of drip irrigation, sprinkler irrigation and center pivot irrigation using various sensors. In India, agriculture has its history. As per the statistics survey, India’s gross domestic product (GDP) growth for agriculture is 6.1% for allied sectors, and about 50% of the workforce depends on farming. The contribution of agriculture to India’s GDP is steadily decreasing with the country’s because of migration of rural youth, less attention on farmers by governments. Still, agriculture is the broadest sector and plays a remarkable role in the overall social fabric of India.  \nGrowing crops is of utmost importance for mankind. Using standard technology increases efficiency and lessens the workload of the farmers. An automated or semi-automated irrigation system must be able to understand the unique requirements of each crop in the agricultural field. The most captivating part of automation is the internet of things (IoT) components that connect with one another and provide relevant information. Smart irrigation uses soil moisture sensors and soil pH sensors that collect real-time information about soil in the field. The current work focuses on recommending the crops based on the real-time values  \nsensed by the sensors. All of these characteristics make the automatic system a practical alternative for improving farming and irrigation effectiveness.  \nSmart farming enables us to monitor environmental conditions and control irrigation systems with the help of IoT. A combination of software and hardware tools ","cbCainFA22cmxtI7","https://ap.wps.com/l/cbCainFA22cmxtI7","pdf",583226,1,9,"English","en",105,"# Introduction\n## Motivation for automated irrigation\n## IoT-based monitoring and sensing\n## Machine learning for crop recommendation","[{\"question\":\"How does the system measure soil conditions for irrigation and crop recommendation?\",\"answer\":\"It uses gypsum block soil sensors to measure dielectric permittivity and derives water-potential information combined with nutrient availability parameters and soil pH.\"},{\"question\":\"Which machine learning approach is used for crop recommendation?\",\"answer\":\"The document states that machine learning approaches are applied to the sensed soil quality data to recommend crops, with random forest listed among the keywords.\"},{\"question\":\"How does the system help farmers in a user-friendly way?\",\"answer\":\"The recommendation model is connected to Telegram to send periodic notifications that support farmers’ decision making.\"}]","Smart irrigation with crop recommendation using machine learning approach | 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