Application of Machine Learning in Agriculture: A Review

Abstract

In the world economy, agriculture plays a vital role. With the ongoing growth of the human population, stresses on the agricultural sector will intensify. With big data innovations and high-performance computing, machine learning has evolved to create new prospects for data-intensive research in the area of multi-disciplinary agro-technology. This paper provides a systematic analysis of studies on machine learning technologies in agricultural production systems. The evaluated works were classified into (a) crop control, including introduction of yield estimation, disease detection, crop quality weed detection, and recognition of species; (b) Livestock Management and (c) water management. The filtering and sorting of the papers discussed illustrate how machine learning technology can support agriculture. Farm management systems are transforming into real-time artificial intelligence powered programmes by implementing machine learning to sensor data that offer rich recommendations and observations for farmer decision support and intervention that contribute to more detailed and quicker decision-making.