Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis.

Near-infrared spectroscopy (NIRS) is widely used for rapid and non-destructive evaluation of feed nutritional quality, but robust calibration remains challenging for heterogeneous multi-product feed datasets. This study evaluated convolutional neural network (CNN)-based models for predicting crude protein (CP) and acid detergent fiber (ADF) using a previously published NIR database containing forage and grain-based feeds. A one-dimensional CNN and two hybrid models, CNN combined with partial least squares regression (CNN+PLS) and XGBoost (CNN+XGBoost), were developed and compared with conventional PLSR calibration models based on either the pooled multi-product dataset or product-specific subsets. Model performance was assessed using an independent internal hold-out test set generated within the same database. For CP prediction, CNN-based models achieved strong performance on the hold-out test set, with testing R² values of 0.98 and RMSEP values of 0.60–0.62, showing a clear reduction in prediction error compared with the global PLSR model. For ADF, CNN and CNN+PLS provided only modest improvements over global PLSR, whereas CNN+XGBoost showed weaker generalization for ADF. Product-wise results further indicated that ADF prediction was more strongly affected by feed matrix and product category than CP prediction. Grad-CAM examples suggested that CNN activation patterns were broadly consistent with known protein- and fiber-related absorption regions, although this interpretation should be regarded as illustrative evidence of spectral coherence rather than direct chemical causality. Overall, CNN-based models, particularly CNN+PLS, showed promise for improving NIRS prediction of CP in heterogeneous feed datasets, while their advantage for ADF was limited. Further validation using independent external datasets and multi-instrument conditions is required before routine implementation.