AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture

Agriculture, an essential driver of economic expansion, is faced by the issue of sustaining an increasing global population in the context of climatic uncertainty and limited resources. As a result, “Smart Farming”, which uses cutting-edge artificial intelligence (AI) to support autonomous decision-making, has become more popular. This article explores how the Internet of Things (IoT), AI, machine learning (ML), remote sensing, and variable-rate technology (VRT) work together to transform agriculture. Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. Furthermore, the precision with which VRT applies water, pesticides, and fertilizers optimizes resource utilization, enhancing sustainability and efficiency. To effectively meet the world’s food demands, this study forecasts a sustainable agricultural future that combines AI-driven approaches with conventional methods.

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Publication Details

Journal
AgriEngineering
Published
2025-03-20
DOI
https://doi.org/10.3390/agriengineering7030089
Citations
29
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
48.99
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article

AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture

Ali Mirzakhani Nafchi, Ahmed Abdalla, Salman Mirzaee, Karishma Kumari
29 citations
AgriEngineering
Smart Agriculture and AI
48.99
article

AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture

Ali Mirzakhani Nafchi, Ahmed Abdalla, Salman Mirzaee, Karishma Kumari
article en
29 citations

Abstract

Agriculture, an essential driver of economic expansion, is faced by the issue of sustaining an increasing global population in the context of climatic uncertainty and limited resources. As a result, “Smart Farming”, which uses cutting-edge artificial intelligence (AI) to support autonomous decision-making, has become more popular. This article explores how the Internet of Things (IoT), AI, machine learning (ML), remote sensing, and variable-rate technology (VRT) work together to transform agriculture. Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. Furthermore, the precision with which VRT applies water, pesticides, and fertilizers optimizes resource utilization, enhancing sustainability and efficiency. To effectively meet the world’s food demands, this study forecasts a sustainable agricultural future that combines AI-driven approaches with conventional methods.

AgriEngineeringVol. 7(3)
South Dakota State University (US)
Openalex Percentile: Top 0%
Smart Agriculture and AI
48.99
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AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture — Ali Mirzakhani Nafchi, Ahmed Abdalla, et al. · AgriEngineering (2025) | TGRS Research Map | TGRS