IoT based intelligent irrigation support system for smart farming applications
<p>India is an agricultural country with an ample amount of arable land that produces wide variety of crops. Growing population and urbanization puts up challenges: more and quality yield in limited area, effective utilization of water resources, inculcating technology with traditional mechani...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2020-05-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/index.php/2255-2863/article/view/20455 |
| _version_ | 1818247669176336384 |
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| author | Neha Kailash NAWANDAR Vishal SATPUTE |
| author_facet | Neha Kailash NAWANDAR Vishal SATPUTE |
| author_sort | Neha Kailash NAWANDAR |
| collection | DOAJ |
| description | <p>India is an agricultural country with an ample amount of arable land that produces wide variety of crops. Growing population and urbanization puts up challenges: more and quality yield in limited area, effective utilization of water resources, inculcating technology with traditional mechanisms, to be faced. A crop irrigation management system with sensor data fetch, transfer and operate functionalities is proposed to meet the expectations. The system comprises of: sensing, data processing and actuator sections, with a network of ambient temperature and humidity at a height and, soil moisture sensor placed at the root zone of the subject. The sensor generated data is compressed and then sent to an FTP server for processing. At the server, a 2-layer Neural Network with 4-Inputs, plant growth, temperature, humidity and soil moisture is used for decision making that controls water supply, fertilizer spray, etc. and a plant is used as the test object. Results show that there is tolerable error in the reconstructed data and 62.5% and 67.5% compression is achieved for ambient temperature, humidity and soil moisture respectively. The decisions are only 2% erroneous when done using Neural Networks using this data. Thus, due to its good data handling, decision making capabilities for precise water usage, being portable and user-friendly, this system proves beneficial in home gardens, greenhouses.</p> |
| first_indexed | 2024-12-12T15:08:22Z |
| format | Article |
| id | doaj.art-aa1a9d8241c944c188983558f2bd45b0 |
| institution | Directory Open Access Journal |
| issn | 2255-2863 |
| language | English |
| last_indexed | 2024-12-12T15:08:22Z |
| publishDate | 2020-05-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj.art-aa1a9d8241c944c188983558f2bd45b02022-12-22T00:20:41ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632020-05-0182758510.14201/ADCAIJ201982758518340IoT based intelligent irrigation support system for smart farming applicationsNeha Kailash NAWANDAR0Vishal SATPUTE1Visvesvaraya National Institute of technology NagpurVisvesvaraya National Institute of technology Nagpur<p>India is an agricultural country with an ample amount of arable land that produces wide variety of crops. Growing population and urbanization puts up challenges: more and quality yield in limited area, effective utilization of water resources, inculcating technology with traditional mechanisms, to be faced. A crop irrigation management system with sensor data fetch, transfer and operate functionalities is proposed to meet the expectations. The system comprises of: sensing, data processing and actuator sections, with a network of ambient temperature and humidity at a height and, soil moisture sensor placed at the root zone of the subject. The sensor generated data is compressed and then sent to an FTP server for processing. At the server, a 2-layer Neural Network with 4-Inputs, plant growth, temperature, humidity and soil moisture is used for decision making that controls water supply, fertilizer spray, etc. and a plant is used as the test object. Results show that there is tolerable error in the reconstructed data and 62.5% and 67.5% compression is achieved for ambient temperature, humidity and soil moisture respectively. The decisions are only 2% erroneous when done using Neural Networks using this data. Thus, due to its good data handling, decision making capabilities for precise water usage, being portable and user-friendly, this system proves beneficial in home gardens, greenhouses.</p>https://revistas.usal.es/index.php/2255-2863/article/view/20455iotsmart farmingdctneural networkautomatic irrigation |
| spellingShingle | Neha Kailash NAWANDAR Vishal SATPUTE IoT based intelligent irrigation support system for smart farming applications Advances in Distributed Computing and Artificial Intelligence Journal iot smart farming dct neural network automatic irrigation |
| title | IoT based intelligent irrigation support system for smart farming applications |
| title_full | IoT based intelligent irrigation support system for smart farming applications |
| title_fullStr | IoT based intelligent irrigation support system for smart farming applications |
| title_full_unstemmed | IoT based intelligent irrigation support system for smart farming applications |
| title_short | IoT based intelligent irrigation support system for smart farming applications |
| title_sort | iot based intelligent irrigation support system for smart farming applications |
| topic | iot smart farming dct neural network automatic irrigation |
| url | https://revistas.usal.es/index.php/2255-2863/article/view/20455 |
| work_keys_str_mv | AT nehakailashnawandar iotbasedintelligentirrigationsupportsystemforsmartfarmingapplications AT vishalsatpute iotbasedintelligentirrigationsupportsystemforsmartfarmingapplications |