Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review
Ghulam Mohyuddin, Muhammad Adnan Khan, Abdul Haseeb, Shahzadi Mahpara, Muhammad Waseem, Ahmed Mohammed Saleh
Abstract
In the era of digital data proliferation, agriculture stands on the cusp of a transformative revolution driven by Machine Learning (ML). This study delves into the intricate interplay between Information and Communications Technology (ICT) and conventional agriculture, emphasizing the role of ML in reshaping farming practices. With the ongoing data tsunami impacting data-driven businesses, the fusion of smart farming and precision agriculture emerges as a beacon of innovation. ML algorithms, analyzing historical and real-time environmental data, soil conditioning, predicts suitable crop for maximum yields, detect diseases, and optimize irrigation in smart farming, facilitating informed decision-making. Precision agriculture benefits from autonomous vehicles and drones, driven by ML, ensuring precision in planting, harvesting, and crop monitoring. Resource efficiency increases as ML optimizes energy consumption, manages fertilizer application, and promotes climate-resilient practices. This comprehensive assessment underscores ML’s pivotal role in maximizing productivity, minimizing environmental impact, and navigating the complexities of modern agriculture.