Strawberry Disease Detection Through an Advanced Squeeze-and-Excitation Deep Learning Model

In this article, an innovative deep learning-driven framework, adapted for the identification of diseases in strawberry plants, is proposed. Our approach encompasses a comprehensive embedded electronic system, incorporating sensor data acquisition and image capturing from the plants. These images are seamlessly transmitted to the cloud through a dedicated gateway for subsequent analysis. The research introduces a novel model, ResNet9-SE, a modified ResNet architecture featuring two squeeze-and-excitation (SE) blocks strategically positioned within the network to enhance performance. The key advantage gained is achieving fewer parameters and occupying less memory while preserving a high diagnosis accuracy. The proposed model is evaluated using in-house collected data and a publicly available dataset. The experimental outcomes demonstrate the exceptional classification accuracy of the ResNet9-SE model (99.7%), coupled with significantly reduced computation costs, affirming its suitability for deployment in embedded systems.