A combined remote sensing and machine learning approach to monitoring crop stress and predicting crop yield

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Optimising crop yield under current and future growth conditions is a key priority for ensuring future food security. The challenges of a changing climate, through increasing temperatures, more variable precipitation, and drought will lead to increased abiotic crop stress, potentially limiting yields and compromising the maintenance of a reliable food supply. The ability of crops to physiologically respond to multiple, simultaneous stresses underpins the resilience of agricultural systems. Technological advances in remote sensing (RS) hold the potential for monitoring crop stress in real-time, however detecting the specific type of abiotic stress from RS data is challenging and compromises both the ability for dynamic intervention and also for understanding the impact on yields. Machine learning (ML) shows great promise for developing new analytical tools to extract key biological information from a range of remotely sensed data. The overall aim of the PhD project is to combine RS measurements with in-depth analyses of plant physiology in order to develop artificial intelligence (AI)/ML algorithms to make effective predictions about different signals of abiotic crop stress and the impacts on crop yield.