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相关概念视频

Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
260
Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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通过视觉语言模型进行交叉模式数据融合,以识别作物疾病.

Wenjie Liu1, Guoqing Wu2, Han Wang1

  • 1School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的视觉语言模型来识别作物疾病,它结合了图像和文本数据. 该模型显著提高了识别作物疾病的准确性,提高了农业生产率.

关键词:
植物疾病的识别和识别跨模型数据融合交叉模型数据融合图像的分类图像的分类.视觉语言模型的模型.

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 农作物疾病严重威胁全球粮食安全和农业生产力.
  • 准确及时识别疾病对于作物产量和质量管理至关重要.
  • 现有的深度学习方法主要依赖于图像数据,往往忽视了有价值的文本信息.

研究的目的:

  • 开发一种新的跨模式数据融合方法来识别作物疾病.
  • 通过整合视觉和文本特征来提高疾病识别的准确性.
  • 利用视觉语言模型,更全面地了解作物叶病.

主要方法:

  • 使用Zhipu.ai多模型生成作物疾病的详细文本描述 (全球,本地病变,颜色-纹理).
  • 将编码的文字描述和图像特征编码成矢量.
  • 采用交叉注意力机制,用于跨层的多式联运特征的代融合.
  • 实施了用于疾病识别的分类预测模块.

主要成果:

  • 拟议的交叉模式融合模型超过了对大豆病,AI挑战2018和PlantVillage数据集的最先进的仅图像方法.
  • 实现了高识别准确度:98.74% (大豆病),87.64% (AI挑战2018) 和99.08% (植物村).
  • 通过显著降低参数数量 (1.14M) 证明了卓越的性能.

结论:

  • 跨模式学习有效地整合了视觉和文本数据,以精确有效地识别作物疾病.
  • 开发的视觉语言模型为农业疾病识别提供了可扩展和准确的解决方案.
  • 这种方法通过先进的人工智能技术提高了提高作物产量和全球粮食安全的潜力.