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Light Acquisition02:16

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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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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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轻量级双阶段特征精细化用于使用ConViTSE的黑色格拉姆叶病分类.

M Anu Kiruthika1, Angelin Gladston2, H Khanna Nehemiah3

  • 1Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, 600025, India. rmtsanudoss@gmail.com.

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概括

一个新的深度学习模型,ConViTSE,准确地检测黑色叶病,提高作物产量. 这种自动化方法为精准农业提供了高效可靠的疾病鉴定.

关键词:
黑草叶病是黑草叶病的一种病.这是一个ConvMixer.诊断作物疾病的诊断作物疾病的诊断功能提取 功能提取功能精细化 功能精细化轻量级的深度学习是轻量级的.挤压和刺激 (SE)变压器变压器变压器

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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
  • 植物病理学 植物病理学

背景情况:

  • 黑 (乌拉德豆) 对印度农业至关重要,但由于叶子疾病而遭受重大损失.
  • 手动识别疾病是耗时且不准确的,阻碍了有效的作物管理.
  • 自动化疾病检测系统对于提高农业生产率和农民生计至关重要.

研究的目的:

  • 开发一种轻量级的混合深度学习模型,用于准确的黑色叶病症分类.
  • 加强特征提取和表示,以改善疾病检测.
  • 评估模型在不同作物中的性能和概括能力.

主要方法:

  • 提出了ConViTSE,这是一个混合架构,集成了ConvMixer,视觉变压器 (ViT) 和挤压和激发 (SE) 块.
  • 集成的本地频道注意力改进 (LCAR) 和全球频道注意力改进 (GCAR) 模块.
  • 在黑色叶病数据集和跨领域数据集 (大米,玉米,小麦) 上训练和评估模型.

主要成果:

  • 在黑色gram数据集上,ConViTSE实现了99.30%的分类准确度.
  • 证明了强大的跨领域概括,精度为98.75% (大米),98.20% (玉米) 和95% (小麦).
  • 在疾病分类准确性方面表现优于传统的深度学习模型.

结论:

  • ConViTSE是用于检测黑色叶病的高度准确和计算效率高的模型.
  • 该模型的强大的通用化潜力支持其在各种作物的精密农业中的应用.
  • 在各种农业环境中,ConViTSE提供了一种实用解决方案,用于实时疾病管理.