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

SUBMITTER: Wang X 

PROVIDER: S-EPMC12568666 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature