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All Journal Bulletin of Electrical Engineering and Informatics
Pathak, Mrunal Kishor
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Electrical & Electronics Engineering

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Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques Santosh, D. Teja; Anuradha, Nandula; Kolukuluri, Madhavi; Gupta, Gaurav; Pathak, Mrunal Kishor; Krishnan, V. Gokula; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6332

Abstract

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.
Co-Authors Anuradha, Nandula Gupta, Gaurav Kolukuluri, Madhavi Krishnan, V. Gokula Raghuvanshi, Abhishek Santosh, D. Teja