This study aims to develop a machine learning model for plant disease detection using deep learning techniques. We utilize a dataset available on Kaggle, which contains images of plant pathogens, to train and evaluate our models. The dataset is structured to help classify plant health based on the presence of various diseases, including bacteria, fungus, and virus, among others.

To tackle this classification problem, three deep learning algorithms will be employed: Convolutional Neural Networks (CNN), ResNet, and MobileNet. These models will be trained on the provided plant pathogen image data to accurately predict the condition of the plant. The expected output of the system is a classification label indicating whether the plant is infected with bacteria, fungus, pests, or virus, or if it is healthy.

The proposed solution will allow users to upload images of plants, and the trained model will predict the plant's health status, providing a valuable tool for early detection and intervention in agricultural practices.

Keywords: Plant Disease Detection, Deep Learning, Convolutional Neural Networks (CNN), ResNet, MobileNet, Plant Pathogens, Image Classification, Bacteria Detection, Fungus Detection, Virus Detection, Pests Detection, Healthy Plants, Machine Learning, Agricultural Technology, Early Disease Detection, Image Recognition.