Prediction for Increasing Yield Production with IoT and AI Using Soil Properties

Pp: 40-61 (22)

* (Excluding Mailing and Handling)

Abstract

'Wireless Sensor Networks and 'The Internet of Things' are the two imminent commonalities in agricultural science that allow the development of less exclusive systems to install, control, and maintain low-power standard protocols. The work makes use of low-cost sensors and IoT platforms to help farmers improve agricultural systems with better yield and reduce insufficient usage of water. Precision agriculture helps in terms of quality of yield, efficiency of product, decrease in the environmental harness, and minimal usage of natural assets. The proposed precision model obtains raw properties of the given soil and achieves an overall accuracy of 93.33% in predicting the ideal crop that can be cultivated for the given soil sample using the KNN algorithm and develops a continuous crop monitoring system for the expected crop based on the predefined crop properties.

Keywords: Arduino, CSV, Digital farming, Energy-efficient, Food processing, GPS, Green Revolution, IoT, Multi-layer perception SMS, Precision agriculture.