Revealing hidden insect damage in sunflower seeds using near-infrared hyperspectral imaging and machine learning

Reliable disease detection of insect-infected sunflower seeds is critical for maintaining seed quality and minimizing economic losses in commercial processing chains. Hyperspectral Imaging (HSI) in the near-infrared region offers a non-destructive way to capture subtle biochemical and structural changes caused by insect feeding, although effective exploitation of its high-dimensional data remains challenging. This study developed a complete seed-level HSI pipeline in the range of 900-1700 nm to distinguish damaged from undamaged seeds based on their spectral characteristics. Two different spectral feature representations were evaluated: Principal Component Analysis feature set and a hybrid feature space combining PCA with eight continuum-removal (CR) metrics describing the depth, area, centroid, and width of moisture and lipid related absorption bands. Using these features, 6 machine learning classifiers namely, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), LightGBM, XGBoost, and PLS-DA, were trained with 5-fold cross-validation and evaluated on a hold-out test set. With PCA-only features, XGBoost and MLP achieved accuracies of 83.53% and 82.93%, respectively. Incorporating CR features improved performance, with the MLP reaching 88.11% accuracy. A soft-ensemble of the optimized MLP and SVM models yielded the best performance, achieving 89.33% accuracy. These results demonstrate that combining dimensionality reduction with CR-based descriptors substantially enhances class separability and offers a practical pathway toward scalable, HSI-based industrial seed inspection systems. • Developed a seed-level NIR-HSI pipeline for hidden insect damage detection • Integrated PCA and continuum-removal features for enhanced separability • Hybrid PCA+CR features improved classification over PCA-only models • ANN–SVM soft ensemble achieved 89.33% detection accuracy • Demonstrates scalable, non-destructive solution for seed quality control