Identification of Maize Seed Quality Using Hybrid Deep-Learning Techniques and Multimodal Sensing Technologies

Reliable recognition of maize seed quality is essential for increased crop production and food safety as well as for automatic seed certification. Traditional visual inspection and single sensor systems can be subjective, laborious to perform, and incapable of identifying subtle or internal defects. The deep learning approach However, current methods have limitations as the segmentation of maize seed, especially bad quality maize seed, is affected by noise and low contrast as well as orientation, meaning that it will be difficult to extract features. The procedure builds on the combination of RGB imaging, hyperspectral and NIR sensing to record complementary spatial, spectral, and biochemical properties of maize seeds. Modality-specific feature extractors are used, spatial features learnt by convolutional neural networks (CNNs) and wavelength dependent information modelled by spectral deep models. We propose a hybrid CNN Transformer model that captures both local texture patterns and global contextual dependencies. In addition, attention-based multimodal fusion mechanism is proposed to automatically and dynamically fuse redundant features across various sensors. Comprehensive experiments on a designed multimodal maize seed dataset show that the proposed framework considerably outperforms traditional machine-learning-based methods, single-modality deep models, and baseline fusion strategies in terms of classification accuracy, F1-score and robustness against illumination and noise perturbations. Explainable AI techniques are used to visually display discriminative regions and crucial spectral bands, providing interpretability for agricultural users. Results indicate strong potential of the proposed system for real-time application at automated seed grading and sorting plant, and it can be considered as an effective, scalable, and reliable tool for intelligent assessment of seed quality in precision farming.