Hardware and embedded algorithms for real time variable rate fertiliser applications

Abstract

Efficient use of fertilisers, in particular the use of Nitrogen (N), is one of the rate-limiting factors in meeting global food production requirements. While N is a key driver in increasing crop yields, overuse can also lead to negative environmental and health impacts. It has been suggested that Variable Rate Fertiliser (VRF) techniques may help to reduce excessive N applications. VRF seeks to spatially vary fertiliser input based on estimated crop requirements, however a major challenge in the operational deployment of VRF systems is the automated processing of large amounts of sensor data in real-time. Machine learning techniques have shown promise in their ability to process these large, high-velocity data streams, and to produce accurate predictions. This paper will use a simulation testing methodology on real hardware to compare two existing machine learning algorithms and a prototype implementation of a newly developed algorithm for their applicability to VRF application.