Deep Learning for Field-Based Cereal Phenomics

Abstract

Plant phenomics has become a critical bottleneck in crop improvement, as the capacity to generate high-quality phenotypic data has not kept pace with advances in genotyping. This thesis integrates deep learning into cereal phenomics through end-to-end, non-destructive pipelines that combine proximal and remote sensing with modern neural network architectures. Near-infrared spectroscopy (NIRS) coupled with convolutional neural networks (CNNs) is applied to rice for predicting amylose content, chalkiness, grain dimensions, and grain shape. Visible to near-infrared (VIS-NIR) hyperspectral imaging is investigated for wheat grain phenotyping using grain-level patches and one-, two-, and three-dimensional CNN architectures, where reformulating cultivar-independent prediction as a classification task improves robustness for moisture and protein. For field-based phenotyping, a deep learning framework estimates rice blast severity from canopy-level images acquired in the Universal Blast Nursery (UBN), and foundation and instance segmentation models are fine-tuned to delineate breeding plots from uncrewed aerial vehicle (UAV) imagery, supporting reliable plot-level trait extraction. The work also treats phenomics models as production systems: pipelines are containerised and deployed across workstations, high-performance computing, and cloud platforms, with systematic evaluation of inference latency and system-level bottlenecks. Together, these contributions demonstrate that deep learning can convert plant phenomics from a limiting factor into a scalable, decision-ready component of modern cereal breeding programs.