AGRICULTURAL CROP RECOMMENDATION SYSTEM BY USING MACHINE LEARNING TECHNIQUES

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, fertilizer needs, 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 production and 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.