Hyperspectral Prediction of Variety, SPAD Value, and Water Content of Oilseed Rape Leaves Using an Improved WGAN-GP
Abstract
Rapid and non-destructive identification of oilseed rape varieties and prediction of leaf Soil Plant Analysis Development (SPAD) value and water content are important for variety evaluation and plant-status monitoring. However, hyperspectral prediction is constrained by small sample size, costly reference measurements, and acquisition-batch differences. Samples were collected over four consecutive days. Day 1 samples were used for training and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) generation. For Days 2–4, 20% of the samples from each day were combined to form a selection-validation set, while the remaining 80% were retained separately as Test sets 1–3. The improved WGAN-GP integrated principal component analysis–Gaussian mixture model (PCA–GMM)-based class assignment, a partial least squares regression (PLSR) consistency loss, physicochemical distribution control, and generated-sample selection. Under single-task modeling, the improved framework enhanced all three tasks across the test sets. On Test set 3, variety accuracy increased from 0.4672 to 0.6100, SPAD root mean square error of prediction (RMSEP) decreased from 6.2201 to 4.6303, and the water-content test-set correlation coefficient (rp) increased from 0.6803 to 0.7842. The improved multi-task model also enhanced all three tasks. These findings support the framework within the investigated three-variety, four-day leaf setting.