Open Access

REVIEW

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Sumit Sow^1,#, Shivani Ranjan^1,#,*, Mahmoud F. Seleiman^2,3, Hiba M. Alkharabsheh^4,*, Mukesh Kumar¹, Navnit Kumar¹, Smruti Ranjan Padhan⁵, Dhirendra Kumar Roy¹, Dibyajyoti Nath⁶, Harun Gitari⁷, Daniel O. Wasonga⁸

1 Department of Agronomy, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
2 Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
3 Department of Crop Sciences, Faculty of Agriculture, Menoufia University, Shibin El-Kom, 32514, Egypt
4 Department of Water Resources and Environmental Management, Faculty of Agricultural Technology, Al Balqa Applied University, Salt, 19117, Jordan
5 Division of Agronomy, ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi, 110012, India
6 Department of Soil Science, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
7 Department of Agricultural Science and Technology, School of Agriculture and Environmental Sciences, Kenyatta University, Nairobi, 43844, Kenya
8 Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

* Corresponding Authors: Shivani Ranjan. Email: ; Hiba M. Alkharabsheh. Email:

(This article belongs to the Special Issue: Abiotic and Biotic Stress Tolerance in Crop)

Phyton-International Journal of Experimental Botany 2024, 93(7), 1569-1598. https://doi.org/10.32604/phyton.2024.052241

Received 27 March 2024; Accepted 30 May 2024; Issue published 30 July 2024

Abstract

Agriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 327 initially identified articles, 180 were deemed pertinent, focusing primarily on AI’s potential in enhancing yield through better management of nutrients, water, and weeds. Taking into account research findings worldwide, we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and determine the best time for irrigation or herbicide application. The present status of AI-driven automation in agriculture holds significant promise for optimizing agricultural input utilization and reducing resource waste, particularly in the context of three pillars of crop management, i.e., nutrient, irrigation, and weed management.

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