Learning latent representations for operational nitrogen response rate prediction
This addresses operational decision-making for nitrogen management in agriculture, but it is incremental as it applies existing representation learning methods to a specific domain.
The study tackled nitrogen response rate prediction by comparing representation learning models (Multilayer Perceptron, Autoencoder, dual-head Autoencoder) against a Random Forest baseline, finding they offer equal or better performance in an operational setting without future weather data.
Learning latent representations has aided operational decision-making in several disciplines. Its advantages include uncovering hidden interactions in data and automating procedures which were performed manually in the past. Representation learning is also being adopted by earth and environmental sciences. However, there are still subfields that depend on manual feature engineering based on expert knowledge and the use of algorithms which do not utilize the latent space. Relying on those techniques can inhibit operational decision-making since they impose data constraints and inhibit automation. In this work, we adopt a case study for nitrogen response rate prediction and examine if representation learning can be used for operational use. We compare a Multilayer Perceptron, an Autoencoder, and a dual-head Autoencoder with a reference Random Forest model for nitrogen response rate prediction. To bring the predictions closer to an operational setting we assume absence of future weather data, and we are evaluating the models using error metrics and a domain-derived error threshold. The results show that learning latent representations can provide operational nitrogen response rate predictions by offering performance equal and sometimes better than the reference model.