Matthew Tom Harrison

According to our database, Matthew Tom Harrison authored at least 7 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Irrigation with Artificial Intelligence: Problems, Premises, Promises.

Hanyu Wei, Wen Xu, Byeong Kang, Rowan Eisner, Albert Muleke, Daniel Rodriguez, Peter deVoil, Victor Oscar Sadras, Marta Monjardino, Matthew Tom Harrison

Hum. Centric Intell. Syst., June, 2024

2023

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review.

Michael Gbenga Ogungbuyi, Caroline Mohammed, Iffat Ara, Andrew M. Fischer, Matthew Tom Harrison

Remote. Sens., October, 2023

Residual correlation and ensemble modelling to improve crop and grassland models.

Renata Sándor, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, Jean-François Soussana, Bruno Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher D. Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Tom Harrison, Ute Skiba, Miko U. F. Kirschbaum, Katja Klumpp, Patricia Laville, Joël Léonard, Raphaël Martin, Raia Silvia Massad, Andrew D. Moore, Vasileios Myrgiotis, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ward N. Smith, Lianhai Wu, Qing Zhang, Gianni Bellocchi

Environ. Model. Softw., March, 2023

Optimizing Crop Management with Reinforcement Learning and Imitation Learning.

Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew Tom Harrison, Carla Sofia Santos Ferreira, Zahra Kalantari, Naira Hovakimyan

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Optimizing Crop Management with Reinforcement Learning and Imitation Learning.

Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew Tom Harrison, Carla Sofia Santos Ferreira, Zahra Kalantari, Naira Hovakimyan

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2021

Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning.

Yun Chen, Juan Guerschman, Yuri Shendryk, Dave Henry, Matthew Tom Harrison

Remote. Sens., 2021

2019

Simple, efficient and robust techniques for automatic multi-objective function parameterisation: Case studies of local and global optimisation using APSIM.

Matthew Tom Harrison, Pier Paolo Roggero, Laura Zavattaro

Environ. Model. Softw., 2019