The goal of this research is to use deep learning and image calibration techniques to create a reliable system for millet quality assessment and classification. As millets are a staple food in many areas, precise and effective techniques for classifying them according to important characteristics like size and colour are needed. A thorough investigation yielded the classification of millets into high- and low-quality categories based on two different factors. Microscopic images played a pivotal role in accurately measuring millet dimensions, forming the first parameter. Through data preprocessing and augmentation, the dataset is tailored to enhance the model’s generalisation capabilities. Image calibration is the first parameter used for the classification of millet categories. Image calibration is a process in which adjustments are made to digital images to correct or standardise them, ensuring accurate and consistent representation. An image was used where the microscopic millet present in the image is the ROI (region of interest), and the actual dimension of the millet was calculated using the Vernier calliper. A relation has been set up between the pixels of the ROI and the actual dimension of the millet. The second parameter was a more complex strategy that included uniformity, colour, texture, and shape. To effectively classify millet, a convolutional neural network (CNN) model was carefully trained to utilise these many properties. This two-pronged methodology offers a strong foundation for determining the quality of millets based on both microscopic qualities and visual traits. By utilising deep learning, combining accurate measurements with sophisticated image processing results in a more complex and thorough assessment of millet quality, highlighting the value of the dual-parameter method.