Fruit Insight - Deep Learning-Based 2D Image Analysis for Evaluating Fruit Quality and Freshness

The detection of calcium carbide in those artificially ripened bananas and an in-depth analysis of fruit, quality, freshness, nutritional values. First, these 2D images of the fruit are run through CNNs which train the model to turn it into things that are of interest, the color, texture and shape properties. Obviously, the levels of accuracy required by the model necessitate pre-processing techniques such as scaling, normalization, and augmentation. It is the same CNN model but through feature extraction and ROI segmentation it recognizes the signs of artificial ripening. Also, an MLP is applied to the regression problem of predicting the chemical and nutritional content of the fruit from the attributes obtained from the CNN as well as additional relevant information. Through the process of categorization and suggesting what to eat and what not to eat (as far as health goes) this approach uses the VGG16 model to recognize the harmful substances from calcium carbide ripening. Not only does this product offer consumer nutrition information that would hopefully encourage them to make a healthier food choice, but this product is a completely automated system that uses real-time data and a scale to ensure food safety based on the knowledge that many ripening practices are hazardous. The system is rated based on its accuracy, precision and RMSE. It works in real time using TensorFlow Serving or TorchServe.