Machine learning approaches to crop yield prediction using multisource agricultural data

Abstract

This paper looks at how machine learning can be used to forecast crop production by combining information from different sources. It looks at the key things that affect crop production, the kinds of information used in today's prediction tools, how the most widely used machine learning methods work, and how these methods are specifically applied to data from farming. The benefits of using machine learning compared to traditional statistical methods are discussed, along with the difficulties that come with putting these technologies into practice. Special care is taken to mix weather, soil, and satellite information, check how good the weather prediction tools are, and help make better farming choices.