Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

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Janmejay Pant, Pushpa Pant, R. P. Pant, Ashutosh Bhatt, Durgesh Pant, Amit Juyal, Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning, Int. J. Anal. Appl., 19 (1) (2021), 91-109.

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Janmejay Pant, Pushpa Pant, R. P. Pant, Ashutosh Bhatt, Durgesh Pant, Amit Juyal

Abstract

Agriculture plays a vital role in the Indian economy. The growth of agriculture sector is based on the type of gift we have got from the nature. It varies state to state, district to district, taluka to taluka, block to block and even village to village. This study is confined to Bhimtal block of Nainital district. The main purpose of agriculture is growing crops and raising livestock. In order to grow the crops several types of agri-inputs are required, among them fertile lands have the great significance in crop cultivation. As far as fertile land is concerned it solely depends on the quality of the soil in terms of producing the nutrients for the crops. The available nutrients present in soil can be evaluated and measured by soil testing tools. The appropriate quantity of soil nutrients supplied to the soil can also be determined by this tool. The quantity of supplied nutrients is based on soil fertility and crop needs. In this study we have classified different soil features such as OC (Organic Carbon), P (Phosphorus), K (Potassium), Mn (Magnesium) and B (Boron). In order to make meaningful inferences and estimates, machine learning techniques especially ANN network with two activation functions relu and tanh are used in this study. For categorizations and predictions we have used village wise soil test report values. This kind of practice will not only help stakeholders to mitigate the expenditure of continuously supplying fertilizers to soil but it would also be cost effective, less time consuming and more profitable for stakeholders. In this regard data was complied, classified, tabulated, presented, analyzed and it can be seen that relu activation function has ensured higher accuracy over tanh activation function. It is expedient and necessary to mention here that out of the five classified soil nutrient parameters relu activation function has shown better performance in respect of four classified soil nutrient parameters while tanh gave better performance in only one classified soil nutrient parameter.

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