Synthetic Hyperspectral Data for Avocado Maturity Classification

The classification of avocado maturity is a challenging task due to the subtle changes in color and texture that occur during ripening and before that. Hyperspectral imaging is a promising technique for this task, as it can provide a more detailed analysis of the fruit’s spectral signature compared with multi-spectral data. However, the acquisition of hyperspectral data can be time-consuming and expensive. In this study, we propose a method for generating synthetic hyperspectral data of avocados. The synthetic data is generated using a generative adversarial network (GAN), which is trained on a small dataset of real hyperspectral im-ages. The generated data is then used to train a neural network for avocado maturity classification. The results show that the neural network trained on synthetic data achieves comparable accuracy to a neural network trained on real data. Additionally, synthetic data is much cheaper and faster to generate than get real data. This makes it a promising alternative for avocado maturity classification.