Published Paper Details:

Agriculture has undergone a digital transformation that has transformed various management functions into artificially intelligent systems in an effort to extract value from the growing amount of data coming from various sources. Machine learning, a branch of artificial intelligence, has a great deal of potential to address many difficulties in the development of knowledge-based farming systems fully reviewing recent scholarly literature using the keywords machine learning" along with crop management, water management, soil management, and livestock management," and current study aims to shed light on machine learning in agriculture. Only journal articles that were released between 2018 and 2020 were deemed eligible. The findings showed that this topic is relevant to various disciplines that support global convergence research. Furthermore, it was noted that crop management was the main focus. There were many different machine learning algorithms used, with artificial neural network-based algorithms being the most effective. Additionally, the most extensively studied crops and animals were wheat and maize, along with cattle and sheep. In order to obtain trustworthy input data for the data analyses, a variety of sensors mounted on satellites and unmanned ground and aerial vehicles have been used.