Use of machine learning and computational methods to parameterise process-based models and develop data drive models predicting Miscanthus yield

Authors Organisations
Type

Student thesis: Doctoral ThesisDoctor of Philosophy

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date2017
Links
Show download statistics
View graph of relations

Abstract

Global climate change is one of the most significant challenges faced by humanity in the 21st century. The adoption of the Paris agreement was the latest action by the international community aimed to limit its impact, by employing a range of strategies for greenhouse gas emissions mitigation. biofuels have a high potential to reduce net CO2 emissions, which are the primary contributor to climate change. Miscanthus is a rhizomatous C4 grass, which has been identified as an ideal candidate for a biofuel crop.  The development of simulation models of Miscanthus plays a crucial role in improving the crop and making it a viable product.  Crop models contribute to our understanding of the biology of the plant and its interaction with the environment, and assist in decision-making in a wide range of processes, including breeding, agriculture, and environmental planning.Crop models are unified in their reliance upon phenotypic data for parameterisation. However, the collection of phenotypic data presents a bottleneck to developing models, as it is a costly and time-consuming process.  This research aims to use machine learning and other computational methods to build models and reduce the cost of data collection, without sacrificing modelling accuracy.  A range of machine learning models of Miscanthus were developed and their precision was validated against an established crop model and real data.  An optimisation method was applied to the previously developed models, which assessed the importance of commonly measured phenotypic variables and identified crucial periods of the growing season for data collection.
This research has shown that machine learning could be a powerful tool in crop modelling, for building accurate predictive models quickly and easily.  It has demonstrated a methodology for minimising data collection costs, which informs experiment design.  Thus, it has the potential to play an important role in Miscanthus breeding and agriculture.

Keywords

  • miscanthus, machine learning, random forests, crop model, k-NN, scatter search, genetic algorithms