RECOMMENDATION OF CROP AND PESTICIDES USING MACHINE LEARNING (original) (raw)

The major resource for improving the economy of India is agriculture. From past farmers followed ancestral faming pattern and regularities within it. A single farmer cannot take action upon improving the crop yield of a nation and does not have enough potential to maximize the crop yield by adopting technical norms within plant growth and improving the yield in a large quantity. Severe change in climatic condition and several other pesticides attack cause shorting of crop yield and also led to food shortage. A simple misguided decision in farming can affect a farmer severe. In recent, there is lot of techniques applied by researchers and those techniques are available to raise the quantity of yield. This in turn changed traditional farming approach and introduced precision farming. Recently data mining performs vital role in identifying plant disease and providing solution prescribing pesticides to plant disease. But this study extends the application of data mining in agriculture to a greater extent. The cultivation of precious crop at right time is the major issues faced by farmer. This study proposes machine learning (ML) approach to resolve it and makes the farmer to choose right crop based on the nutrition content and quality of soil. The machine learning algorithms chosen for this study are Random forest, decision tree and K-nearest neighboring. Some of the factors mainly considered for recommendation of plant are humidity, rainfall, pH value, soil moisture. The recommended technique makes farmer to take decision on improving the crop yield; recommending crops as per climatic condition and quality of land.