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English(EN) Fusing Perceptual Vision Experts with Multimodal Large Language Models for Explainable Plant Disease Diagnosis: From Benchmark Imagery to Real-World Robotic Field Validation

AI框架助力植物病害诊断和水果分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了用于农业应用的高级AI框架，重点关注植物病害诊断和水果分类。第一项研究介绍了H²MAF，该框架将EfficientNet-B3和ConvNeXt-Tiny等视觉模型与Gemma 4 E4B和Qwen3.5 4B等多模态大语言模型（MLLMs）融合，以提供可解释的植物病害诊断和风险评估。第二项研究提出了一种使用TinyCLIP的轻量级视觉-语言框架，用于早期绿色水果分类，并针对NVIDIA Jetson硬件上的边缘部署进行了优化。 AI

影响 这些进展展示了AI通过精确的病害诊断和自动化的水果分析来提高农业效率的潜力，从而实现更好的作物管理和机器人应用。

排序理由 两篇学术论文详细介绍了用于农业应用的新型AI框架。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI框架助力植物病害诊断和水果分类

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两篇学术论文详细介绍了用于农业应用的新型AI框架。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ranjan Sapkota, Konstantinos I. Roumeliotis, Pengyao Xie, Nikolaos D. Tselikas, Lirong Xiang, Manoj Karkee ·

    融合感知视觉专家与多模态大语言模型以实现可解释的植物病害诊断：从基准图像到真实世界机器人田间验证

    arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework (H$^{2}$MAF), combining decision-level fusion of EfficientNet…

  2. arXiv cs.CV TIER_1 English(EN) · Ranjan Sapkota, William Bu, Chen Chen, Yunjun Xu, Manoj Karkee ·

    面向商业果园早期解剖学绿果分类的轻量级多模态视觉语言框架

    arXiv:2608.24935v1 Announce Type: new Abstract: Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic thinning, crop-load management, and other precision orchard operations. This stu…

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