相关实验视频

Updated: Jun 1, 2025

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

BDMANGO: 一个图像数据集,用于识别基于果叶的果品种

Mohammad Manzurul Islam¹, Md Jubayer Ahmed¹, Mahmud Bin Shafi¹

¹Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.

|January 22, 2025

概括

为农业中的机器学习创建了一个新的数据集,BDMANGO. 它包含来自孟加拉国的六种果叶品种的6,696张图像,有助于准确的识别和研究.

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:

高质量的数据集对于推进农业机器学习至关重要.
在孟加拉国,识别果叶类型和品种是一个挑战.
现有的数据集可能不能充分代表果品种的多样性.

研究的目的:

创建一个全面的,公开可访问的数据集,用于果叶的识别.
支持农业应用中的机器学习研究和开发.
为了促进孟加拉国多样化的果品种的认可.

主要方法:

收集了来自孟加拉国各个地点的六种果品种 (Amrapali,Banana,Chaunsa,Fazli,Haribhanga,Himsagar) 的图像.
使用谷歌Pixel 6a和iPhone XR以640x480分辨率拍摄的图像,在白色背景下拍摄两侧叶子.
应用图像增强技术 (旋转,翻转,移位,剪切,变焦) 来将数据集从837张图像扩大到6,696张图像.

主要成果:

BDMANGO数据集包括6,696张图像,包括837张原始图像和5,859张增强图像.
数据集包括六种不同的果品种,对农业研究至关重要.
图像是用白色背景拍摄的,以尽量减少噪音,并增强用于机器学习的特征提取.

结论:

BDMANGO数据集为培训和验证用于果叶分类的机器学习模型提供了宝贵的资源.
这一数据集将提高模型在识别特定果叶的有效性,即使使用细分算法.
该数据集的可用性支持孟加拉国及其他国家的农业研发.

关键词:

人工智能的人工智能是人工智能.计算机视觉计算机视觉计算机视觉图像的分类图像的分类.果树叶的叶子果的叶子果品种种类型

更多相关视频

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

相关概念视频

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.