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RiGaD:用于评估发芽率和密度的米苗的空中数据集

Trong Hieu Luu¹, Hoang-Long Cao^1,2, Quang Hieu Ngo¹

¹College of Engineering, Can Tho University, Can Tho 910900, Viet Nam.

|December 5, 2024

概括

这项研究引入了来自无人机图像的苗的新数据集,有助于早期生长监测. 农民可以使用这些数据来评估发芽率和密度,改善作物管理.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
遥感遥感遥感遥感

背景情况:

无人驾驶飞行器 (UAV) 为农业进步提供了具有成本效益的数据收集.
米苗的早期生长阶段对于作物建立和长期产量至关重要.

研究的目的:

展示来自无人机空中图像的标记大米苗的数据集.
为了促进基于机器学习的米苗发芽和密度的评估.

主要方法:

在各种环境条件下使用无人机收集米苗的空中图像.
采用适应值来隔离大米幼苗.
将苗木分为单个,集群和未定义的类别.
利用数据增强生成了5364个标记图像.

主要成果:

创建了一个由5364个标记的苗图像组成的综合数据集.
该数据集使机器学习模型能够用于发芽率和密度评估.
该方法允许有效地隔离和分类苗.

结论:

开发的数据集支持在早期生长期间监测苗的健康和活力.
该资源有助于农民了解和管理苗木机构.
基于无人机的数据收集和分析增强了精准农业实践.

关键词:

数据集数据集数据集图像处理图像处理机器学习机器学习大米大米大米大米大米种子的种子,就是种子.播种播种播种播种无人机无人机无人机是什么?

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Light Acquisition

Light Acquisition

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.