Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection

Early and accurate detection of plant diseases is crucial for making informed decisions to increase the yield and quality of crops through the decision of appropriate treatments. This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. In particular, we introduced a transformer module, a fusion of the SPP and C3TR modules, to synthesize features in various sizes and handle uneven input image sizes. The proposed model combined with transformer-based long-term dependency modeling and convolution-based visual feature extraction to improve object detection performance. To optimize a model to a lightweight version, we integrated the proposed transformer model with the Ghost module. Such an integration acted as regular convolutional layers that subsequently substituted for the original layers to cut computational costs. Furthermore, we adopted the SIoU loss function, a modified version of CIoU, applied to the YOLOv8s model, demonstrating a substantial improvement in accuracy. We implemented quantization to the YOLOv8 model using ONNX Runtime to enhance to facilitate real-time disease detection on strawberries. Through an experiment with our dataset, the proposed model demonstrated [email protected] characteristics of 80.30%, marking an 8% improvement compared to the original YOLOv8 model. In addition, the parameters and complexity were reduced to approximately one-third of the initial model. These findings demonstrate notable improvements in accuracy and complexity reduction, making it suitable for detecting strawberry diseases in diverse conditions.