A Survey on Different Data Mining Techniques for Crop Yield Prediction
DOI:
https://doi.org/10.26438/ijcse/v7i1.738744Keywords:
Agriculture, crop yield prediction, data mining, machine learning techniqueAbstract
Crop growing is measured as the stamina of India, is the improvement of plant for foodstuff, bio-fuel, counteractive plants and other harvest for behind and enhancing human life. Farming is an unique business crop creation which is contingent on different attributes such as soil, climate, irrigation, precipitation, insect killer weeds, fertilizers, nurturing, temperature, harvesting and other factors. An accurate crop yield prediction helps support decision makers in the agriculture sector to envisage the yield effectively. Data mining techniques play a vital role in the study of data for crop yield prediction. Data mining is the computing method of discovering patterns in hefty datasets involving methods at the connection of machine learning, artificial intelligence, record and system database. This piece of writing presents a detailed examination of various techniques planned for crop yield prediction. At first, dissimilar techniques developed by previous researchers are calculated in detail. Then, a relative analysis is carried out to know the precincts of each technique and afford a suggestion for further enhancement in crop yield prediction successfully.
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