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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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相关实验视频

Updated: Jul 1, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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用深度卷积神经网络为远程传感图像分析作物分类进行Dipper throated优化.

Youseef Alotaibi1, Brindha Rajendran2, Geetha Rani K3

  • 1College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia.

PeerJ. Computer science
|March 4, 2024
PubMed
概括
此摘要是机器生成的。

一种新的方法,Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC),使用遥感图像显著提高了作物分类的准确性. 这种进步有助于粮食安全和环境监测.

关键词:
农作物的分类作物分类深度学习是一种深度学习.迪珀喉优化算法的优化算法图像处理 图像处理遥感图像的远程传感图像.

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 农业科学 农业科学
  • 计算机科学 计算机科学

背景情况:

  • 遥感技术的进步需要有效的作物分类,以确保粮食安全和环境监测.
  • 传统方法在高分辨率遥感数据的准确性和可扩展性方面扎.
  • 准确的作物分类对于可持续农业和资源管理至关重要.

研究的目的:

  • 为了开发一种新的作物分类技术,Dipper Throated Optimization使用基于深度卷积神经网络的作物分类 (DTODCNN-CC).
  • 通过遥感图像提高作物分类的准确性和效率.
  • 为了实现各种粮食作物的高分类准确度.

主要方法:

  • 使用GoogleNet架构 (深度卷积神经网络 - DCNN) 来进行特征提取.
  • 用户使用Dipper Throated Optimization (DTO) 来对GoogleNet模型进行超参数调整.
  • 使用极端学习机器 (ELM) 进行作物分类,参数通过修改的正弦弦算法 (MSCA) 进行微调.

主要成果:

  • DTODCNN-CC方法显示出明显更高的作物分类准确度.
  • 实验分析证实,与现有的最先进的深度学习方法相比,它具有更高的性能.
  • 优化的GoogleNet和ELM模型实现了强大的特征提取和分类.

结论:

  • DTODCNN-CC为使用遥感数据进行准确和高效的作物分类提供了一个有前途的解决方案.
  • 这种技术在农业,粮食安全和环境监测方面有很大的应用潜力.
  • 该研究强调了将优化算法与远程传感应用的深度学习相结合的有效性.