Soil Analysis for Fertilizer Prediction using IoT and Machine Learning

  • N. V. S. Sai Teja, P. Sri Gowtham, T. Lakshmi Surekha, P. Gopala Krishna

Abstract

As India is an agriculture background country, preserving the soil nutrients is essential. Agriculture is a major stream that helps us to meet the growing need for food in the country. Since agriculture is highly dependent on the soil, maintenance of soil minerals is necessary to reduce the drop in the crop yield. To achieve this, the easiest way is by applying Fertilizers. Measurement of soil Nitrogen (N), Phosphorus(P) and Potassium(K) is necessary to determine the suitable Fertilizer. The work here designs an NPK sensor with Light Dependent Resistor (LDR) and Light Emitting Diodes to measure the NPK mineral values present in the soil. The data sensed by the designed NPK sensor from the selected agricultural fields is sent to the cloud using Thingspeak tool. A database is created by recording LDR data and NPK mineral values. On this database logistic regression algorithm is applied and the machine learning model is trained. The predicted data from the Machine learning model is now saved for future reference. The Results of the analysis are used for predicting the suitable fertilizer from the Fertilizer database and then the suggestions are sent to the farmer's device using GSM module. This way the Soil testing is done effectively and the results are sent to the farmer much faster than most of the soil tests that are available today.

Published
2020-06-01
How to Cite
N. V. S. Sai Teja, P. Sri Gowtham, T. Lakshmi Surekha, P. Gopala Krishna. (2020). Soil Analysis for Fertilizer Prediction using IoT and Machine Learning. International Journal of Control and Automation, 13(02), 1497 - 1504. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/32925
Section
Articles