High-Throughput Machine Learning Pipelines for Forest Genetics: Predicting Wood Growth Stresses in Eucalyptus via Spatiotemporal Environmental and Phenotypic Data

The utilization of fast-growing plantation timber, particularly Eucalyptus species, is heavily constrained by inherent growth stresses that complicate solid wood processing. Traditional methodologies for screening seed orchards and evaluating age-age correlations rely on manual, low-frequency biometric sampling and chemical assays, creating a severe data bottleneck in forest genetics. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for forestry phenotypes. By deploying distributed Python middleware integrated with eXtreme Gradient Boosting regression, the proposed system programmatically ingests multi-modal data streams encompassing sap chemistry, cell wall biometrics, and localized micro-climate telemetry. The system translates these inputs into a dynamic Growth Stress Predictive Index, instantly identifying low-stress genetic variants within massive seed orchards. Preliminary architectural evaluations demonstrate that automating the phenotypic data synthesis layer significantly accelerates the identification of genetic controls over growth stresses, providing wood science researchers with a deterministic, highly scalable tool for optimizing plantation timber processing.