Title: A smart crop, irrigation system and fertiliser prediction using IoT and machine learning

Authors: Prince Rajpoot; Ram Avtar; Amit Kumar Pandey; Shivendu Mishra; Vikas Patel; Amrendra Singh Yadav; Shikha Choudhary; Kumkum Dubey; Digvijay Pandey

Addresses: Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, India ' Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan ' Department of Applied Science and Humanities, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Electrical Engineering, Rajkiya Engineering College, Ambedkar Nagar, India ' ABV-Indian Institute of Information Technology and Management, Gwalior, Madya Pradesh, India ' Mahamaya College of Agriculture Engineering and Technology, Ambedkar Nagar, Uttar Pradesh, India ' Department of Computer Science, United University Prayagraj, Prayagraj, Uttar Pradesh, India ' Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

Abstract: Agriculture plays a crucial role in building a nation's economy. Environmental change due to global warming affects groundwater levels and crop production. Other problems also affect crop productivity, like workforce availability, changing soil nature, etc. These circumstances lead to an update of the traditional agriculture model. To enhance productivity, we have proposed a machine learning (ML) and IoT-based model that may overcome these problems with some intent. The proposed ML-based model is trained by the datasets of crops and irrigation models with efficient conditions to predict the best crop type according to the environmental conditions and suitable fertiliser with the optimised irrigation system for the area.

Keywords: agriculture; artificial intelligence; irrigation; IoT; sensor.

DOI: 10.1504/IJGW.2024.138434

International Journal of Global Warming, 2024 Vol.33 No.2, pp.107 - 124

Received: 25 Apr 2023
Accepted: 16 Jul 2023
Published online: 03 May 2024 *

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