The challenges of urbanization, land degradation, water scarcity, and climate change are threatening agri-cultural systems and food security. Therefore, it is essential to manage land and water resources sustainably to improve productivity and address these challenges. Digital agriculture, which involves the use of smart systems and advanced farming practices incorporating Artificial Intelligence (AI) and Internet of Things (IoT) technologies, can provide solutions for sustainable agriculture and climate change adaptation. This study aims to fill the research gap by understanding the recent pulse and current trends of significant works published on AI and IoT-assisted land and water management (LWM) research. A bibliometric analysis of 436 English language articles published over the last three decades (1991-2021) was conducted. The study revealed a significant shift in research trends in 2010, with over 60 AI techniques utilized under different AI and IoT frameworks for LWM. The highly adopted AI techniques include Artificial Neural Networks (ANN-9.85%), Adaptive Neuro-Fuzzy Inference System (ANFIS -5.98%), Support Vector Regression (SVR-3.87%), Random Forest (RF-3.16%), and Multilayer Perceptron-ANN (MLP-ANN-2.81%). China, India, Iran, Australia, and the USA were identified as pioneers in the field, while Italy, Spain, and Saudi Arabia showed potential as emerging countries but with low collaboration links. The study also discussed the current limitations and challenges of AI and IoT technologies. Future research focus areas for AI and IoT were identified, and it was recommended to conduct comparative evaluations of AI techniques to determine the most effective approach for specific LWM domains.
Patel, A; Kethavath, A; Kushwaha, NL; Naorem, A; Jagadale, M; Sheetal, KR; Renjith, PS
Review of artificial intelligence and internet of things technologies in land and water management research during 1991-2021: A bibliometric analysis
Engineering Applications Of Artificial Intelligence