Ioannis Athanasiadis bio photo

Ioannis Athanasiadis

Professor of Artificial Intelligence
Geo-information Science and Remote Sensing Lab
Wageningen University & Research

Email University Page Twitter LinkedIn Google Scholar ORCID ACM DL DBLP Web of Science

Learning latent representations for operational nitrogen response rate prediction

C. Pylianidis, I. N. Athanasiadis

Abstract

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.

Download full text in pdf format

cover image Published as:
C. Pylianidis, I. N. Athanasiadis, Learning latent representations for operational nitrogen response rate prediction, Computing Research Repository, AI for Earth Sciences Workshop at 10th Int'l Conf Learning Representations (ICLR 2022), 2022, doi:10.48550/arXiv.2205.09025.


You might also enjoy (View all publications)