Detection of Insect-Damaged Maize Seed Using Hyperspectral Imaging and Hybrid 1D-CNN-BiLSTM Model

Abstract

Maize (Zea mays L.) holds a pivotal position in various domains, making maize seeds' germination rate and quality crucial for agricultural production. Insect-infested seeds can impair their germination rate, resulting in substantial economic losses. Nevertheless, research on rapid detection methods for insect infestation in maize seeds remains insufficient. In this study, we employed hyperspectral data within the range of 930–1866 nm alongside intelligent algorithms to address the classification of healthy and insect-infested maize seeds. The 1D-CNN-BiLSTM and SVM models were applied to diverse combinations of spectra and texture features. The results indicated that the 1D-CNN-BiLSTM model exhibited clear advantages over SVM in handling multi-source data. Additionally, ANOVA identified the 1160 nm / 1310 nm band ratio as optimal. The 1D-CNN-BiLSTM model, utilizing texture features from this band ratio image, exhibited exceptional classification performance with just two band images. The GLCM + 1D-CNN-BiLSTM model garnered the best results, with the F1-score and accuracy of 0.96, respectively. This approach streamlined the band number, reducing model complexity and rendering it promising for practical applications. Consequently, the results of this study provide new insights and methods for classifying insect infestation maize seeds.