Artificial Intelligence in Electric Agricultural Machinery: A Comprehensive Review of Technologies, Applications, Challenges and Future Directions

Author

Sudhanshu S. Nagpure, Prakash R. Kolhe, Atul R. Patil, Vishnu D. Jadhav


Abstract

 The global agricultural sector faces mounting pressures from population growth climate change labor shortages and rising fossil-fuel expenses necessitating a transition toward sustainable efficient and intelligent farming systems. This review comprehensively synthesizes the convergence of artificial intelligence AI and electrification in modern agricultural machinery. Conventional diesel-powered machinery while reliable contributes significantly to greenhouse gas emissions pollution and high operational costs. The electrification of agricultural equipmentranging from tractors and sprayers to weeders reapers and autonomous robotsoffers high drivetrain efficiency reduced local emissions and compatibility with renewable energy sources. However challenges such as limited battery capacity charging infrastructure and variable operational loads require advanced control strategies. By integrating AI technologies such as machine learning deep learning computer vision reinforcement learning and fuzzy logic electric agricultural machinery can achieve autonomous navigation smart energy management predictive maintenance and precise site-specific operations. This review highlights current technological applications key sensor integrations existing technical and environmental challenges future research directionsincluding edge AI digital twins and swarm roboticsand essential research gaps to facilitate the widespread commercialization of next-generation smart agricultural machinery.


Keywords

Artificial Intelligence; Electric Agricultural Machinery; Sustainable Agriculture; Autonomous Navigation; Energy Management; Precision Farming; Computer Vision; Predictive Maintenance



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References


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