Hyperspectral-based classification of individual wheat plants into fine-scale reproductive stages.

Field trials play an essential role in developing genetically modified and genome-edited biotechnology plants, as they assess plant growth, yield, and potential unintended effects. Australian biotechnology field trials are regulated by federal protocols that mandate accurate forecasting of flowering times. Currently, this relies on labour-intensive and subjective visual field inspections of individual wheat plants at defined growth stages (Zadoks growth stages Z37, Z39, and Z41). To enable automatic forecasting, hyperspectral and red-green-blue (RGB) images were captured in the greenhouse, and hyperspectral reflectance data were acquired in a semi-natural environment. Support Vector Machine classification achieved F1 scores (0.832) for pre-anthesis growth stage classification through the combined use and systematic comparison of three spectral transformations, including Standard Normal Variate, Hyper-hue, or Principal Component Analysis, which together outperformed reliance on any single transformation. After feature selection, F1 scores (0.752) could be achieved with only five wavelengths. Furthermore, the SNV transformation demonstrated robust performance under limited training conditions, maintaining high classification accuracy and strong generalizability across varying data sizes. These findings highlight the effectiveness of transformation-enriched data and optimized feature selection for accurate growth stage classification, providing a low-cost approach to reduce manual inspection burdens and strengthen biosafety during biotechnology field trial practices.