A data‐driven image extraction and analysis pipeline for plant phenotyping in controlled environments
构建多光谱图像提取分析流程，实现多作物表型自动化量化。
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BTSR-Net: Boundary–topology guided structural refinement for high-resolution remote sensing image segmentation
提出BTSR-Net，用边界与拓扑引导提升高分辨率遥感影像分割的结构连续性。
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FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection
提出频域自适应分割网络FDUNet-LRSA，用于甘薯病害自动巡检。
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A lightweight hybrid network with inverted residuals and state space model for remote sensing image segmentation
提出IRUM-Net混合网络，结合Mamba与CNN实现遥感图像轻量高精度语义分割。
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CuFormer U-Net: a transformer-enhanced multi-task network for cucumber leaf disease detection and segmentation
提出CuFormer U-Net多任务网络，同时实现黄瓜叶病分类与病斑分割。
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Intelligent Automated Crop Monitoring System Based on Unmanned Aerial Vehicles and Deep Learning for Smart Agriculture
构建无人机RGB影像深度学习分割与空间矢量化框架，实现作物监测优先级评估。
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A gradient-enhanced lightweight network for peanut leaf disease segmentation in complex field scenarios
提出梯度增强轻量网络，用于复杂田间场景下花生叶部病害分割。
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Segmentation of Cactus Diseases Using Machine and Deep Learning)
该研究用随机森林和多种深度学习模型对仙人掌病害进行分割诊断，并比较性能。
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CropRowSeg: A Lightweight Model for Seedling Crop Row Image Segmentation Combining Multi-scale CNN and Transformer（融合多尺度 CNN 与 Transformer 的苗期作物行图像轻量化分割模型）
提出轻量级作物行分割模型CropRowSeg，并设计中心线引导的条带标注方法。
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PSPE-UNet: Projection-based Similarity Prototype Embedding UNet for Apple Leaf Disease Segmentation
提出PSPE-UNet，用投影相似原型嵌入分割苹果叶片病害区域。
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Prompt Engineering in the Segment Anything Model: Methodologies, Applications, and Emerging Challenges
系统综述了SAM及其变体中的提示工程方法、应用与挑战。
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Vision Transformers Enable Advanced Plant Phenotyping in Controlled Environments
比较阈值法、U-Net和预训练ViT在植物分割中的跨物种泛化能力。
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A dynamic multi-scale feature fusion and hierarchical attention network for leaf segmentation
提出动态多尺度特征融合与层级注意力网络，用于叶片实例分割，提升精度。
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