Table 2_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.docx by Xinrui Wang (488330)

“…</p>Methods<p>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). …”

Table 1_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.xlsx by Xinrui Wang (488330)

“…</p>Methods<p>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). …”