Pineapple disease detection system using MobileNeTv2 model / Muhammad Nu’man Hakim Abdul Aziz and Iman Hazwam Abd Halim - Abdul Aziz, Muhammad Nu’man Hakim; Abd Halim, Iman Hazwam (College of Computing, Informatics and Media, UiTM Perlis, 2023)
Disease in plant has been a major challenging factor for agricultural field. To counter this problem a quick and accurate model could help in detecting plant disease. This project focus on pineapple disease detection using deep learning. Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection. As the method that going to be use for the disease detection an advance system that going to be use for this project is Neural Network. Since this project is going to use image classification convolutional neural network is going to be use since it was a type of artificial neural network that usually being used in image recognition that specifically for processing pixel data. Since the dataset that going to be used is based on picture that being capture then it was suitable for this project. The goal of this project is to test the dataset of pineapple disease with Convolutional Neural Network by using MobileNetV2 model architecture through mobile app to classify and identify pineapple fruit diseases. This project dataset is trained by using large dataset that have different type of pineapple disease and healthy image of pineapple. Lastly this project is going to test the accuracy of the proposed system in detecting Pineapple fruit disease by using Mobilenetv2 model architecture.