AGRICULTURAL CROP RECOMMENDATION SYSTEM BYUSING MACHINELEARNINGTECHNIQUES

Abstract

Uncertainty in agricultural output is a problem for coastal areas like California. More population and land
area should lead to more output, yet this is not the case. For decades, farmers have relied on word of
mouth, but climate change has rendered this information obsolete. Factors and parameters in agriculture
allow for the derivation of insight into agri-data. Using the data at hand, machine learning methods
construct a clear model to aid in prediction. Crop forecast, crop rotation, water needs, fertilizerneeds, and
crop protection are only some of the ag problems that may be addressed. It is important to have an
efficient approach to assist crop cultivation and to give a hand to farmers in their productionand
management because of the fluctuating climatic conditions of the environment. Future farmers might
benefit from this, perhaps making agriculture more successful. It is possible to provide a system of
recommendations to a farmer to aid the small scale agriculture production through data mining.