Detection of insect-damaged sunflower seeds using near-infrared hyperspectral imaging and machine learning

Insect damage can significantly affect seed germination rates and overall seed quality, resulting in notable economic losses. Detecting insect-damaged seeds is vital for upholding food safety standards and satisfying consumer expectations in confectionery sunflower markets. To tackle this issue, this study explores the potential of hyperspectral imaging combined with machine learning to accurately classify damaged and undamaged sunflower seeds. Spectral data were acquired and preprocessed using principal component analysis (PCA) to reduce dimensionality while retaining essential spectral information. Machine learning techniques, specifically multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), gradient boosting (GB), and partial least squares discriminant analysis (PLS-DA), were trained and evaluated based on the spectral features. The results showed that MLP achieved the highest classification performance with an accuracy of 0.91 and an F1-score of 0.91, followed by SVM with an accuracy of 0.89 and an F1-score of 0.89. LGBM and RF also performed well, both achieving an accuracy of 0.88 and an F1-score of 0.88, while XGB and GB recorded accuracies of 0.85 and 0.86, respectively. In contrast, PLS-DA demonstrated the lowest performance, with accuracy falling to 0.65 and an F1-score of 0.64. These findings underscore the effectiveness of machine learning in utilizing hyperspectral data for precise seed quality assessment. Its integration into the seed sorting process can enhance seed inspections, food safety, damage scoring for scientific investigations, and ensure that only high-quality seeds are chosen for planting.