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

Abstract

Introduction Pepper 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. Methods To 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. Results To 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. Discussion Comparative 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.

Plant phenotyping relevance

コショウ葉画像のセグメンテーション手法を開発し、複数データセットで性能検証しており、植物器官の観測・抽出方法が研究の中心である。

Code and data availability

The supplied blocks describe pepper leaf image datasets from a private repository of the Nanchang Academy of Agricultural Sciences, with no public data deposit, code release, or availability URL mentioned. No paper-specific public asset qualifies.

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