Design of an Embedded Machine Learning Based System for an Environmental-friendly Crop Prediction Using a Sustainable Soil Fertility Management

Abstract

Most of the existing precision agriculture solutions recommend the use of fertilizers as a remedy to poor soil fertility and to boost yields. Such solutions cause environmental degradation in the long run mainly due to the overuse of fertilizers. There is therefore, a need for a system to ensure that farmers can practice precision farming in terms of a sustainable soil management approach so as to attain high yields while at the same time conserving the environment. In this research, a design and simulation of an embedded machine learning based system to predict the best crop to grow with minimal use of fertilizers with an aim of conserving the environment is presented. The system senses different real time soil parameters on a daily basis, integrates them with forecast weather information and uses embedded machine learning technique to determine which crop would grow best under the existing soil conditions so as to minimize fertilizer use. In addition to crop prediction, the system helps farmers to monitor the soil nutrients evolution so that action can be done on real time. The results are either displayed on the device or sent to the farmer’s mobile phone. This is a move from the existing solutions that depend on cloud analytics and do not consider the change of soil conditions on time in making the predictions and decisions since this is expensive when done at the cloud. The implementation of the proposed solution is expected to not only lead to high productivity and reduced costs but also conserve the environment.