This whitepaper is an encapsulation of the research conducted by Cyient in introducing a system designed for assessing fruit ripeness utilizing a hyperspectral camera paired with a 2D Convolutional Neural Network (CNN) architecture. The research project is a part of the Action Learning Project (ALP) within the Technology Leadership Program (TLP). The development of this architecture took place on Google Colab, a cloud-based platform offering GPU access to boost the performance of deep learning models like CNNs.
Evaluation of the architecture involved utilizing a dataset containing ripening avocados, kiwis, and various other fruits, subjecting them to testing with diverse sets of features. The architecture's effectiveness was further improved through the integration of an optimal HyperSpectral Image (HIS) visualizer and the incorporation of additional features, such as those provided by the Gray-Level Co-occurrence Matrix (GLCM). This architecture demonstrated robust performance on the Colab platform, producing results consistent with findings from similar studies conducted previously.