Prediction Of Soil Quality Using Machine Learning Techniques

Computer Engineering

Agribusiness is the foundation of India. In India 50 % of the remaining task at hand depends upon agribusiness. Commitment of agriculture part in Indian economy is higher than some other division in India. In any case, Farmers utilized customary strategy for developing harvests which comes to less profitability of yields. Additionally, a soil erosion and is integration is likewise a principle motivation to less profitability of yields. This will impact in diminishes fruitfulness level. Loss of soil supplements through different courses is likewise motivation to diminish soil richness level. The supplements like potassium (K), nitrogen (N) and phosphorus (P) are basic for the development of a plant. The advancement in agriculture is important to tackle these issues in agribusiness part and shrewd cultivating is the appropriate response. Farmers usually follow a method called crop mutation after every consequent crop yield. The crop mutation allows the soil to regain the minerals that were used by the crop previously and use the left-over minerals for cultivating the new crop. To know if the soil has reached the point where it is unfit to yield the particular crop, farmer has to experience a loss in yield. One financial year for a farmer is very crucial to accept the loss. This paper implements a that would help in maintaining the soil fertility consistently. This method is traditionally implemented in many countries where the change in crop is done after a loss in yield for cultivating the same crop continuously. There are three soil parameters that come into consideration when we have to predict the soil quality. This method suggests the solution for the above stated problem using Machine Learning Techniques. This paper suggests a software enabled solution considering crucial soil parameters and soil factors to predict the soil quality.

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