TY - JOUR
T1 - AI-Driven Future Farming
T2 - Achieving Climate-Smart and Sustainable Agriculture
AU - Kumari, Karishma
AU - Nafchi, Ali Mirzakhani
AU - Mirzaee, Salman
AU - Abdalla, Ahmed
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/3/20
Y1 - 2025/3/20
N2 - 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.
AB - 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.
KW - artificial intelligence (AI)
KW - Climate smart
KW - internet of things (IoT)
KW - machine learning (ML)
KW - variable-rate technology (VRT)
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=hbku_researchportal&SrcAuth=WosAPI&KeyUT=WOS:001453468900001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.3390/agriengineering7030089
DO - 10.3390/agriengineering7030089
M3 - Review article
SN - 2624-7402
VL - 7
JO - AgriEngineering
JF - AgriEngineering
IS - 3
M1 - 89
ER -