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相关概念视频

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 7, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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使用卷积变压器进行番茄成熟度识别.

Asim Khan1,2, Taimur Hassan3, Muhammad Shafay2,4

  • 1Department of Mechanical Engineering, Khalifa University, Abu Dhabi, UAE.

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|December 22, 2023
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概括
此摘要是机器生成的。

一种新的卷积变压器方法准确地对番茄成熟度进行分类,其性能优于现有的技术. 这一进步提高了番茄收获,分类和质量控制的效率.

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科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确的番茄成熟度分类对于农业应用,如收获和质量控制至关重要.
  • 现有的方法可能缺乏对各种现实世界条件所需的精度.
  • 开发强大的自动化系统对于现代农业至关重要.

研究的目的:

  • 提出一种使用卷积变压器进行番茄成熟度分类的新方法.
  • 引入一个新的,多样化的番茄图像数据集 (KUTomaData),用于训练深度学习模型.
  • 为了证明拟议的卷积变压器对最先进的方法的优越性.

主要方法:

  • 开发了一种混合卷积变压器架构,将CNN和变压器结合起来.
  • 创建了KUTomaData数据集,包含在各种照明和视角下约700张图像.
  • 评估了KUTomaData和两个公共数据集 (Laboro Tomato,Rob2Pheno Annotated Tomato) 的框架.

主要成果:

  • 卷积变压器显著超过了最先进的方法.
  • 获得的平均精度得分为58.14% (KUTomaData),65.42% (Laboro Tomato) 和66.39% (Rob2Pheno Annotated Tomato) 的平均精度得分.
  • 该框架有效地处理了混乱和封闭的番茄实例.

结论:

  • 拟议的卷积变压器是对番茄成熟度分类的高效方法.
  • 库托马数据数据集为农业深度学习研究提供了宝贵的资源.
  • 这项工作有可能提高番茄生产和加工的效率和准确性.