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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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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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可解释的深度学习模型用于自动分类木叶病症.

Md Nahiduzzaman1,2, Muhammad E H Chowdhury2, Abdus Salam1

  • 1Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.

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概括

一个新的计算机视觉模型,PDS-CNN,准确地识别了像生和斑点这样的木叶病. 这种可解释的人工智能工具有助于丝农业专家提高作物产量和丝生产.

关键词:
沙普利的添加式解释 (SHAP)深度智能可分离的卷积卷积.可解释的人工智能 (XAI)树叶子 树叶子平行卷曲的并行卷曲是什么

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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:

  • 丝种植,负责90%的全球原始丝,面临降低产量由于木叶病.
  • 手动识别这些疾病是耗时且容易出现错误的.
  • 目前还没有深度学习模型用于检测木叶病.

研究的目的:

  • 开发和评估一种新的深度学习模型,以准确有效地识别木叶病.
  • 为了解决手动疾病诊断在羊毛种植的局限性.
  • 为丝种植专家创建一个可解释的AI工具.

主要方法:

  • 收集和注释的图像健康和生病的木叶 (叶子生,叶子斑点).
  • 利用图像增强,从最初的764张图像数据集中生成6,000张合成图像.
  • 开发了一种轻量级的平行深度分离卷积神经网络 (PDS-CNN) 模型.
  • 应用了SHapley添加式解释 (SHAP) 来确保模型可解释性.

主要成果:

  • PDS-CNN模型实现了高精度:95.05%的三类分类和96.06%的二元分类.
  • 该模型重量轻,只有0.53万个参数,8个层,大小为6.3MB.
  • 在准确性,参数数量,层数量和大小方面,PDS-CNN的表现优于已有的深度转移学习模型.
  • SHAP可视化证实了与专家评估一致的模型预测.

结论:

  • 可解释的基于AI的PDS-CNN提供了一个有效的解决方案,用于准确分类叶疾病.
  • 这项技术可以显著帮助大白种植专家在早期发现疾病和提高产量.
  • 开发的模型展示了轻量级,可解释的AI在农业应用中的潜力.