Food Disease Detection via Hyperspectral Imaging and Deep Learni

The Apple disease dataset is converted into hyperspectral images by generating synthetic spectral bands based on RGB values. These images are used to train deep learning models like AlexNet, GoogLeNet, and CNN for classification. Features are extracted from each hyperspectral image, and the models are trained to classify apple diseases. The dataset is split into training and testing sets, with performance evaluated using accuracy, precision, recall, and F1-score. Quantum-enhanced machine learning (QSVC) can also be applied, utilizing QuantumKernel for classification. This approach helps identify the most effective model for accurate hyperspectral disease detection.