GreenGuard:

An Android-Based Application for Early Disease Detection of Indoor Plant

Abstract

Numerous individuals like the hobby of taking care of indoor plants, which provides both aesthetic and health benefits. However, they may be susceptible to a variety of disorders, as is the case with other plants. Manually identifying plant diseases is a time-consuming, error-prone, and unreliable process that impedes the ability to effectively identify and prevent them. The challenges can be surmounted by the early and precise detection of interior plant diseases by implementing new technologies such as Deep Learning and Machine Learning . In this research, we introduce 'GreenGuard,' a revolutionary solution for indoor plant devotees. This study employs critical deep-learning models to identify plant diseases, including ResNet50, VGG16, EfficientNetB0, EfficientNetB5, and EfficientNetB7. Models such as ResNet50 and EfficientNetB7 are more effective in detecting maladies in indoor plants regarding accuracy, precision, recall, and F1 score. Our Android application
enables the early detection of common diseases that affect indoor plants, particularly those on fashionable balconies, and provides solutions by utilizing ResNet50's advanced image processing techniques.