Using Artificial intelligence in sustainable agriculture and irrigation management

Sanaz Mohammadi; Iman Hajirad

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Using Artificial intelligence in sustainable agriculture and irrigation management

Sanaz Mohammadi

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1},

Iman Hajirad

¹ Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
² Ph.D. Student, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

Academic Editor: Junye Wang

Published: 11 October 2024 by MDPI in The 8th International Electronic Conference on Water Sciences session Numerical and Experimental Methods, Data Analyses, Digital Twin, IoT Machine Learning and AI in Water Sciences

Abstract:

Background and Aims: This study explores the application of artificial intelligence (AI) in irrigation management to optimize soil, water, and fertilizer consumption in agriculture. The growing world population and the subsequent increase in demand for agricultural products, coupled with climate change, have put increasing pressure on water resources. Therefore, efficient soil and water management practices are necessary. AI techniques such as machine learning, neural networks, and the Internet of Things (IoT) are being utilized to analyze various data sources including weather patterns, soil conditions, crop types, and water levels.

Methods: The working method in this research includes collecting information from the articles listed in the References Section and examining them in detail in order to identify the types of sub-branches of artificial intelligence used in studies related to climate change in different sectors of agriculture. More than 41,000 full titles in 130 reference databases were simultaneously reviewed. A total of 26 primary studies were selected to form the basis of this review.

Results: This study emphasizes how studies on water and soil management heavily rely on artificial intelligence (AI) techniques based on artificial neural networks (ANNs) and fuzzy logic. ANNs have shown great performance and are primarily utilized for machine learning-based solutions. Compared to other AI techniques or even well-known regression methods, these networks are frequently more effective. In the assessment of soil and water issues, ANN-based solutions have also been shown to be more effective than traditional equations, particularly in situations when limited data are available.

Conclusions: This enables farmers to make informed decisions in agricultural operations to safeguard and manage water and soil resources effectively. In conclusion, this study underscores the potential benefits of integrating AI technologies in irrigation management for sustainable agricultural practices in an increasingly water-scarce world.

Keywords: water and soil management, fuzzy logic, artificial neural networks, climate change, irrigation

Keywords: Water and Soil Management, fuzzy logic, artificial neural networks, Climate Change, Irrigation.

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Sanaz Mohammadi

Iman Hajirad