How will the next-generation of sensor-based decision systems look in the context of intelligent agriculture? A case-study

• Published in: Field crops research 2021-08, Vol.270, p.108205, Article 108205
• Main Authors: Colaço, A.F., Richetti, J., Bramley, R.G.V., Lawes, R.A.
• Format: Article
• Language: English
• Subjects: Crop reflectance sensors; Decision support systems; Nitrogen; On-farm experimentation; Soil moisture sensors
• ISSN: 0378-4290; 1872-6852
• DOI: 10.1016/j.fcr.2021.108205

The development of cost-effective, digitally based decision support systems is a key challenge in the optimization of farm management. Yet, the majority of sensor-based decision tools which support fertiliser management have relied on simplistic mechanistic frameworks normally informed by a single sensor. This study used a 20-year nitrogen (N) experiment on winter wheat (Triticum aestivum L.) to test a range of approaches for N decision support systems, including commercial sensor-based options and a novel, multivariate, data-driven approach. The latter was based on a non-mechanistic framework in which various digital variables were trained directly against optimum N application rates using machine learning. It was hypothesized that such a method would enhance our ability to handle system complexity, resulting in higher accuracy for the decision, as compared to current farm management or to available sensor-based options, both of which are normally underpinned by mechanistic methods. Results showed that the proposed approach was able to predict the optimal N rate with an RMSE of 16.5 kg N ha–1 (R2 = 0.79). This method was also the only one that was statistically superior (p