多核初始增强视觉转换器用于植物叶病的识别
Sk Mahmudul Hassan1, Kumar Sekhar Roy2, Ruhul Amin Hazarika2
1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. mahmudul.hassan@manipal.edu.
Scientific reports
|August 23, 2025
概括
准确的植物疾病鉴定对于作物保护至关重要. 本研究引入了Inception-Enhanced Vision Transformer (IEViT) 模型,该模型有效地使用实验室和现实世界的图像识别植物疾病,其性能优于现有的方法.
科学领域:
- 农业科学
- 计算机视觉
- 机器学习
背景情况:
- 准确的植物疾病识别对于作物产量和粮食安全至关重要.
- 手动识别是劳动密集型的,需要专门的专业知识.
- 现有的计算机视觉模型往往与现实世界的现场条件不符.
研究的目的:
- 开发一个强大的深度学习架构来识别植物疾病.
- 解决当前模型在处理各种图像条件 (实验室与现场) 的局限性.
- 提高植物疾病自动诊断的准确性和效率.
主要方法:
- 提出了一个Inception-Enhanced Vision Transformer (IEViT) 架构.
- IEViT集成了本地和全球特征提取能力.
- 用多个不同的内核大小的过器来进行高效的功能学习.
- 在五个不同的数据集上进行实验, 包括实验室和现场图像.
主要成果:
- 与最先进的深度学习模型相比,IEViT模型表现出更高的性能.
- 在多个数据集中实现了高准确率:99.23% (果叶),99.70% (大米),97.02% (豆类),76.51% (麻豆叶) 和99.41% (植物村).
- 拟议的架构与现有模型相比,使用更少的参数实现了这些结果.
结论:
- 启动增强视觉变压器 (IEViT) 是一种强大而高效的植物疾病识别架构.
- IEViT有效处理图像条件的变化,在实验室和现场数据上表现良好.
- 这种方法为农业中自动化,准确和可扩展的植物疾病诊断提供了有希望的解决方案.
相关概念视频
02:16Light 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.
8.6K
02:00Photoreceptors and Plant Responses to Light
24.5K
Light plays a significant role in regulating the growth and development of plants. In addition to providing energy for photosynthesis, light provides other important cues to regulate a range of developmental and physiological responses in plants.
24.5K