Applications of Statistical Machine Learning Algorithms in Agriculture Management Processes

In any country's economic growth, agriculture plays a crucial function. In crops management, machine learning techniques are mainly employed, following the control of farming conditions and the management of animals. They are used in agriculture to anticipate crop yield and quality and the production of livestock. As the population increases, the climate changes are frequent and the resources are limited, it becomes a challenge to meet food demands of the people today. Machine learning (ML) is the mechanism for driving this advanced technology. It allows to the machine for learn without being programmed directly. The agricultural machinery enabled by ML and Internet of Things (IoT) is an important part of the future farm revolution. There has been a rigorous discussion on IOT based network technology involving network architecture and layers. In this research paper described a systematic examination of agricultural with ML applications. The focus areas are the prediction of soil factors including organic carbon and moisture content in the prediction of crop yields, diseases and the detection of weeds in crops as well as species.