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Light Acquisition02:16

Light Acquisition

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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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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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利用主动学习和注意力-CNN基于无人机多谱数据对植被进行分类.

Sheng Miao1, Chuanlong Wang1, Guangze Kong1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, China.

Scientific reports
|December 28, 2024
PubMed
概括

这项研究引入了一个深度学习模型,使用主动学习从无人机图像中准确识别植被. 它显著降低了标签成本,同时保持了高准确度,非常适合远程传感应用.

关键词:
卷积块注意力模块是一个卷积块注意力模块.卷积神经网络是一种卷积神经网络.封闭式的全聚变门.无人机远程传感 无人机远程传感植物的分类 植物的分类

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能
  • 生态生态学 生态生态学

背景情况:

  • 准确的植被类型识别对于生态监测至关重要.
  • 传统方法通常需要大量的标记数据,增加成本.
  • 无人驾驶飞行器 (UAV) 遥感提供高效的数据采集.

研究的目的:

  • 开发一种深度学习模型,使用无人机多谱数据准确地对植被进行分类.
  • 实施积极学习策略,以尽量减少数据标签成本.
  • 为了增强模型的特征提取能力,以区分相似的植被类型.

主要方法:

  • 利用来自无人机遥感的多谱数据.
  • 采用积极学习策略,最低可信度评分和数据池抽样.
  • 开发了一种深度学习模型,采用语义细分门封闭的全融合模块和双重注意力机制.

主要成果:

  • 在20%的标签成本下实现了93.2%的平均准确性.
  • 与训练样本有限的其他模型相比,证明了更高的分类准确性.
  • 在全注释成本下达到95.32%的平均准确性,只有2%的差异,同时节省了80%的注释成本.

结论:

  • 积极学习有效地识别了高价值的样本,大大降低了注释成本.
  • 拟议的模型提高了植被分类的准确性和效率.
  • 现场调查验证了该模型在识别表面植被覆盖类型方面的可靠性能.