A Survey of Machine Learning Appliacations In Decision Making To Improve Farming - Bissa, Ankita; Kushwaha, Meena; Patel, Mayank (International Journal of Computer Sciences and Engineering, 2025)
Machine learning has developed with huge information innovations and best outline to make new opportunity for data science in the multi-disciplinary agriculture domain. In this paper, we present a far reaching review of research devoted to applications of machine learning in agrarian generation frameworks. The works broke down were classified in (a)crop management, including applications on yield expectation, infection identification, weed discovery, crop quality, and species acknowledgment; (b) domesticated animals management, including applications on creature welfare and animals generation; (c) water management; and (d) soil management. The review reports the application of machine learning techniques with artificial neural networks, Bayesian networks, support vector machines and k-means. The filtering and classification of the reviewed articles show how farming will benefit from machine learning advancements. By applying machine learning to sensor information, cultivate administration frameworks are advancing into ongoing artificial insight empowered projects that give rich proposals also, bits of knowledge for agriculturist choice help and activity.