YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading.

Journal: Plant methods
Published Date: Jun 24, 2025

Abstract

BACKGROUND: Citrus leaf diseases significantly affect production efficiency and fruit quality in the citrus industry. To effectively identify and classify citrus leaf diseases, this study proposed a classification approach leveraging deep learning techniques (YOLOV8 equipped with CSPPC, MultiDimen, SpatialConv, YOLOV8-CMS). Additionally, a segmentation method was utilized to extract leaf and lesion areas for disease severity grading based on their pixel ratio.

Authors

  • Hongyan Zhu
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
  • Dani Wang
    Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541004, China.
  • Yuzhen Wei
    School of Information Engineering, Huzhou University, Huzhou, 313000, China.
  • Pengcheng Wang
    Department of Plant Protection, Henan Institute of Science and Technology, Xinxiang, China.
  • Min Su
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.

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