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The task is difficult because lesion scale varies from incipient micro-spots to merged areas and because lesion boundaries are often low-contrast and blend into healthy tissue. 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Plant Sci. 16:1727075 .  \ndoi: 10.3389/fpls.2025.1727075  \nCOPYRIGHT  \n© 2025 Sun, Li, Pan, Zhu, Yang, Shao, Guo and Xin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nGradient-guided boundaryaware selective scanning with multi-scale context aggregation for plant lesion segmentation  \nGuanqun Sun 1†, Tianshuo Li 1†, Yizhi Pan 1,2†, Zidan Zhu 1, Tianhua Yang 1, Feihe Shao 1, Jia Guo 3* and Junyi Xin 1*  \n1School of Information Engineering, Hangzhou Medical College, Hangzhou, Zhejiang, China,  \n2School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan,  \n3 Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan  \nIntroduction: Plant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture. However, the task remains challenging due to large variations in lesion scale—ranging from minute incipient spots to coalesced regions—and ambiguous, low-contrast boundaries that blend into healthy tissue.  \nMethods: We present GARDEN, a Gradient-guided boundary-Aware RegionDriven Edge-refiNement network that unifies multi-scale context modeling with selective long-range boundary refinement. Our approach integrates a Multi-Scale Context Aggregation (MSCA) module to harvest contextual cues across diverse receptive fields, forming scale-consistent lesion priors to improve sensitivity to tiny lesions. Additionally, we introduce a Boundary-aware Selective Scanning (BASS) module conditioned on a Gradient-Guided Boundary Predictor (GGBP) . This module produces an explicit boundary prior to steer a Mambabased 2D selective scan, allocating long-range reasoning to boundary-uncertain pixels while relying on local evidence in confident interiors.  \nResults: Validated across two public plant disease datasets, GARDEN achieves state-of-the-art results on both overlap and boundary metrics. Specifically, the model demonstrates pronounced gains on small lesions and boundaryambiguous cases . Qualitative results further show sharper contours and reduced spurious responses to illumination and viewpoint changes compared to existing methods.  \nDiscussion: By coupling scale robustness with boundary precision in a single architecture, GARDEN delivers accurate and reliable plant lesion segmentation. This method effectively addresses key challenges in the field, offering a robust solution for automated disease analysis under challenging real-world conditions.  \nKEYWORDS  \ngradient-guided, Mamba, multi-scale context aggregation, plant lesion segmentation, selective scanning, state space models  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \n","cbCaiq774tSYKNhL","https://ap.wps.com/l/cbCaiq774tSYKNhL","pdf",4684583,15,"English","# Introduction\n## Plant lesion segmentation challenges\n# Methods\n## GARDEN framework and MSCA module\n## Boundary-aware selective scanning with GGBP\n# Results\n## Performance on public plant disease datasets\n# Discussion\n## Boundary precision and scale robustness","[{\"question\":\"What problem does plant lesion segmentation solve?\",\"answer\":\"It labels disease regions in plant images at the pixel level, supporting early diagnosis, severity measurement, and targeted treatment in precision agriculture.\"},{\"question\":\"Why is the segmentation task challenging?\",\"answer\":\"Lesions vary greatly in scale and appearance, and their boundaries often have low contrast that blends into healthy tissue.\"},{\"question\":\"What are the key components of the GARDEN method?\",\"answer\":\"GARDEN combines Multi-Scale Context Aggregation (MSCA) for scale-consistent lesion priors with a boundary-aware selective scanning module guided by a Gradient-Guided Boundary Predictor (GGBP).\"}]","Gradient-guided boundary-aware selective scanning with multi-scale context aggregation for plant lesion segmentation | PDF",1790706350,38]