Jaideep Singh Bhullar
University of British Columbia
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http://doi.org/10.37648/ijrst.v13i04.009
Agriculture is one of the most important sectors feeding the world's population. Traditional farming methods face major challenges such as climate change, soil degradation, and inefficient resource use. Machine learning has emerged as a powerful tool in modern agriculture, offering predictive analytics, automation, and precision farming solutions. By leveraging ML, farmers can make informed decisions regarding crop yield estimation, disease detection, soil health analysis, and efficient irrigation management. Various types of ML-techniques in the domain supervised learning include techniques as Random Forest and Support Vector Machines, whereas on the unsupervised side includes K-Means Clustering; also deep learning comes into consideration along with reinforcement learning. These applications are then compared with its problems along with furthering its prospects and real-case study examples in how ML works with agricultural productivity optimizations. The study points out the significance of integrating ML with IoT and remote sensing technologies, and correspondingly enhancing the data collection and analysis process. In addition, we discuss some economic and environmental advantages linked with the adoption of ML-based agricultural solutions, thereby showing how technology can contribute to sustainable farming practices. Challenges would be data scarcity, model interpretability, and high implementation costs, and potential solutions for those challenges would be discussed as well. Finally, future research directions are proposed for improving the access and efficiency of ML in agriculture, which is going to be a stepping stone for smart farming innovations.
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