Abstract
Abstract: This research covers deep learning approaches, a supervised machine learning model for fruit illness diagnosis, and a convolutional neural network-based fruit grading system. We employed a Visual Geometry Group (VGG) which is a part of the Convolutional Neural Network (CNN) and it produced an accurate result. Fruit disease detection is a tough task for a manual inspection system, so we have designed a system that detects the fruit disease and grades it. In recent times the machines are incorporated with a high-speed computing hardware device that allows the developers to develop the complex system using different types of machine learning models, and algorithms for better results and near accuracy. Using these advanced models of neural networks, a good model is built for classifying the better fruit. The dataset is taken from kaggle.com, and various sorts of fruit photos were utilized to train and test the model, resulting in an accurate result. Key Terms: Supervised machine learning model, Convolutional Neural Network, Visual Geometry Group, Pre-Processing, Feature Extraction, Classification, Gray Scale Conversion, Noise Removal, Thresholding, and Image Sharpening.
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