Severity Grading of Early Visible Symptoms of Sugarcane Pokkah Boeng Disease Based on Multi-Scale Dynamic Feature Fusion and Channel-Spatial Interaction: An On-Device Framework

Sugarcane pokkah boeng disease is a fungal disease caused by pathogens in the Fusarium fujikuroi species complex (FFSC). Early visible symptoms include chlorosis and yellowing at the base of young leaves, leaf wrinkling, and morphological abnormalities, which may differ only subtly from healthy leaves and make manual inspection and severity assessment difficult. Existing studies have paid limited attention to severity grading of these early visible symptoms, while some models remain too complex for mobile deployment. To address these issues, this study established a four-grade severity standard using leaf colour alteration, leaf morphological abnormalities, and visible abnormalities in the growing-point region as visual symptom anchors, and constructed an image dataset for sugarcane pokkah boeng disease. A lightweight grading framework based on MobileNetV2 was then developed for this task, incorporating multi-scale dynamic feature fusion with a fast channel-spatial interaction module to enhance multi-scale and channel-spatial representations of severity-related visual features. The proposed method achieved Accuracy, Precision, Recall, and Macro-F1 of 96.29% ± 0.73%, 95.32% ± 0.79%, 95.99% ± 0.78%, and 95.43% ± 0.77%, respectively, outperforming the main comparison models overall. On-device accuracy was 96.53% under both FP32 and INT8 precision. After INT8 quantisation, the ONNX model size decreased from 8.63 MB to 2.57 MB, and single-image inference latency from 30.819 ± 4.625 ms to 10.794 ± 0.999 ms. This study provides a lightweight solution for image-based visual severity grading and mobile-assisted assessment of early visible symptoms of sugarcane pokkah boeng disease, thereby supporting technical support for mobile-assisted severity assessment and disease management.