Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm

Abstract

Introduction: Accurate assessment of sugarcane leaf disease severity is crucial for early warning and effective disease control. Methods: In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data—including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content—were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). After min-max normalization, six classification models—KNN, AdaBoost, Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and XGBoost—were developed, and the Sparrow Search Algorithm (SSA) was employed to optimize hyperparameters for enhanced performance. Results: Results demonstrate that SSA significantly improved the classification capability of all models. The SSA-XGBoost model achieved the best performance, with Precision, Recall, F1 Score, and Accuracy all exceeding 0.9186, and a comprehensive PRFA score of 0.9326. When validated on an independent dataset from Gengma County, the model achieved an overall accuracy of 0.91, indicating strong generalization ability and field applicability. Discussion: Compared to image-based deep learning approaches, the proposed method offers advantages in terms of data accessibility, computational efficiency, and model transparency, making it well-suited for rapid on-site diagnosis in agricultural settings. This study provides an efficient and reliable technical framework for intelligent diagnosis and early warning of sugarcane disease severity.