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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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基于人工智能的实时疾病诊断在植物使用深度学习驱动的CNNs.

D Devarajan1, Randa Allafi2, Marwa Obayya3

  • 1Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India, 611002. devarajand@ymail.com.

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概括

这项研究引入了深度学习框架,用于实时植物疾病诊断. 人工智能模型从图像中准确识别疾病,使作物健康管理更快,更可靠,并减少产量损失.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.植物疾病诊断诊断 植物疾病诊断精准农业 精准农业 精准农业实时监控实时监控智能农业智能农业

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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:

  • 传统的植物疾病诊断是缓慢的,劳动密集的,容易出现人为错误.
  • 目前的方法不适合大型作物系统,需要实时,准确的诊断.
  • 早期检测对于最大限度地提高作物产量和最大限度地减少损失至关重要.

研究的目的:

  • 使用深度学习开发和验证实时植物疾病诊断系统.
  • 提高植物疾病检测的速度,准确性和可扩展性.
  • 支持精准农业和可持续的植物卫生管理.

主要方法:

  • 使用深度学习 (PDD-DL) 框架实施植物疾病诊断.
  • 使用卷积神经网络 (CNN) 来自动分析植物图像.
  • 对常见作物的模型验证,有可能对不同类型的疾病进行再培训.

主要成果:

  • PDD-DL模型实现了高性能指标:98.32%的准确性,97.85%的精度,98.14%的回忆率和97.99%的F1-score.
  • 每张图像的实时推断速度为42.6ms,证明了系统的效率.
  • 该模型有效地区分健康和患病的植物.

结论:

  • 深度学习,特别是CNN,为传统的植物疾病诊断提供了更快,更可信,更可扩展的替代方案.
  • 开发的框架提高了诊断植物疾病的准确性和速度.
  • 这项技术有助于精准农业,并促进可持续的作物管理实践.