Option: Data Engineering and Web Technologies Deep Learning Image-based Plant Diseases Classification for Agricultural Systems

Abstract

Plant diseases are a big problem for farming and the safety of food around the world. We suggest a way to automatically classify plant diseases using deep learning and a custom dataset made up of more than 24,000 images from different sources. To see how color affects model performance, the dataset was preprocessed, augmented, and split into two formats: colored and grayscale. We built a Convolutional Neural Network (CNN) model from the base up and also improved two pretrained models, ResNet50 and VGG16, using transfer learning methods. Our tests show that the VGG16 model was the best at classifying both colored and grayscale datasets. CNN worked well on colored datasets, and RESNET worked well on grayscale datasets. The results also show that grayscale images can work just as well, which shows how flexible deep learning models are when it comes to different types of input. In general, this study shows how important the model architecture, preprocessing strategy, and size and design of the dataset are for making strong AI solutions for precision agriculture