AUTHOR=Fang Jiangxiong , Liu Huaxiang , Zhang Shiqing , Hu Hui , Gu Huaqi , Fu Youyao 

TITLE=AMS-MLP: adaptive multi-scale MLP network with multi-scale context relation decoder for pepper leaf segmentation

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1515105

DOI=10.3389/fpls.2025.1515105

ISSN=1664-462X

ABSTRACT=IntroductionPepper leaf segmentation plays a pivotal role in monitoring pepper leaf diseases across diverse backgrounds and ensuring healthy pepper growth. However, existing Transformer-based segmentation methods grapple with computational inefficiency, excessive parameterization, and inadequate utilization of edge information.MethodsTo address these challenges, this study introduces an Adaptive Multi-Scale MLP (AMS-MLP) framework. This framework integrates the Multi-Path Aggregation Module (MPAM) and the Multi-Scale Context Relation Mask Module (MCRD) to refine object boundaries in pepper leaf segmentation. The AMS-MLP includes an encoder, an Adaptive Multi-Scale MLP (AM-MLP) module, and a decoder. The encoder’s MPAM fuses five-scale features for accurate boundary extraction. The AM-MLP splits features into global and local branches, with an adaptive attention mechanism balancing them. The decoder enhances boundary feature extraction using MCRD.ResultsTo validate the proposed method, extensive experiments were conducted on three pepper leaf datasets with varying backgrounds. Results demonstrate mean Intersection over Union (mIoU) scores of 97.39%, 96.91%, and 97.91%, and F1 scores of 98.29%, 97.86%, and 98.51% across the datasets, respectively.DiscussionComparative analysis with U-Net and state-of-the-art models reveals that the proposed method significantly improves the accuracy and efficiency of pepper leaf image segmentation.