Application of Colorimetry to Determine Soil Fertility through Naive Bayes Classification Algorithm

Fertility of the soil is considered most important criterion in any agriculture practice. Nutrients present in the soil define its fertility. Mineral nutrients such as Nitrogen (N), Potassium (K), Phosphorous (P) are vital for plant growth and food production. Lack of adequate knowledge amongst the farmers about the various parameters in farming like the soil fertility, amount of fertilizer to be used, leads to degradation of the overall soil quality. In this paper, we have represented a system to test the soil fertility by using the principal of colorimetry. Colorimetry is a technique in which we measure the amount of light absorbed by the color developed in the sample. An aqueous solution of the soil sample is prepared using extracting agents and is subjected to the photodiodes of the color sensor. The solution develops a color due to reaction of nutrients in the soil with chemicals. The output by the color sensor is calibrated with standard values present in the database. To verify the results obtained by the color sensor we use the Naive Bayes classification algorithm. This algorithm classifies the intensity values of the soil solutions into three class labels namely low, medium, high. After applying the Naive Bayes classifier, we can predict the accuracy of the intended system. The intended system is thus beneficial to reduce the time required for testing the soil fertility and determining the accuracy of our results.