Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound
提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。
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#作物病害
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。
Agricultural Visual Recognition: A Novel Privacy-Preserving Framework Based on 4D Chaos and Mamba Networks
提出结合四维混沌加密与Mamba网络的隐私保护作物病害识别框架。
Deep Learning-Based Crop Disease Detection Using EfficientNet-B3 for Smart Agriculture
Deep Learning-Based Crop Disease Detection Using EfficientNet-B3 for Smart Agriculture。Research Square
Explainable Deep Learning-Based Potato Leaf Disease Detection and Severity Assessment for Smart Agriculture in Bangladesh
Explainable Deep Learning-Based Potato Leaf Disease Detection and Severity Assessment for Smart Agriculture in Bangladesh。Research Square
Artificial intelligence for crop disease detection and management: A bibliometric analysis of global research trends
通过文献计量分析，梳理了2016-2026年AI在作物病害检测与管理中的研究趋势。
Lightweight real-time detection of sweet potato virus disease by a hybrid CNN–Transformer network
提出Swin-SPVDNet混合CNN-Transformer网络，实现甘薯病毒病轻量级实时检测。
Multi-Agent Vision-Language Debate Framework for Zero-Shot Crop Disease Diagnosis (VIDA+PANDA)
本文提出VIDA+PANDA多智能体视觉语言框架用于零样本作物病害诊断,包含两个阶段:VIDA多VLM智能体独立分析作物叶片图像建立基线性能;同行锚定命名审议架构(PANDA)在选定的智能体群体中引入结构化多轮辩论,智能体交换推理、批评同行预测、通过基于证据的讨论修订决策,反谄媚机制阻止无支持的共识转变。在CDDM基准上使用来自四个独立提供商的七个异构VLM,在完全零样本设置下,非参与方GPT-5作为独立评判者评估。框架引入三个语义度量:语义标签相似度(SLS)、推理特异性(RS)、智能体间推理收敛度(IRC)。实验结果表明协作多智能体审议提高了个体诊断性能和语义对齐,较弱智能体增益最大。原文框架发表于Frontiers in Plant Science。
基于影像组学与可解释机器学习的香蕉病害快速识别
原文：Radiomics-driven and explainable machine learning for rapid characterization of Fusarium wilt and Black Sigatoka in banana crops。研究于2026年7月28日发表，聚焦利用影像组学和可解释机器学习快速表征香蕉枯萎病与黑斑病，属于作物病害智能识别方向。当前公开检索摘要未呈现完整实验细节，故不对模型精度作额外推断。