Transforming Apple Disease Detection with Advanced Deep Learning: A Hybrid Approach Using Mobilenetv3 Small and Res MLP

Abstract

The identification of plant diseases through image analysis is crucial in precision agriculture. Traditional methods rely on extensive manual inspection, which is time-consuming and prone to error. Deep learning approaches, particularly convolutional neural networks (CNNs), offer a promising solution for automating this process. This research focuses on preprocessing, augmenting, and analyzing image data to build a robust model capable of distinguishing between healthy and diseased apple leaves. The proposed hybrid model combines MobileNetV3Small and Res MLP architectures, achieving a balance between accuracy and computational efficiency. The novelty of this research lies in the integration of advanced preprocessing techniques and a hybrid deep learning model specifically designed for apple disease detection.