ABSTRACT
In the field of machine learning, there is a large variety of different methods that can be used to create a model with the ability to anticipate data that has not yet been observed and to learn prediction rules based on previous data. The current paradigm in farming is known as “smart agriculture,” which views a farm as a collection of independent units and searches for inconsistencies between output and demand at each individual unit. The ultimate purpose of implementing smart farming practices is to increase agricultural yields and revenue while simultaneously lowering operational costs. Farmers who are continually seeking for ways to better their processes utilize them. Farmers that are progressive use them. Machine learning algorithms, on account of the precision with which they do predictions, pave the way for the creation of solutions that are smart for farming.
