Multiclass Weeds Dataset for Image Segmentation

The Multiclass Weeds Dataset for Image Segmentation comprises two species of weeds: Soliva Sessilis (Field Burrweed) and Thlaspi Arvense L. (Field Pennycress). Weed images were acquired during the early growth stage under field conditions in a brinjal farm located in Gorakhpur, Uttar Pradesh, India. The dataset contains 7872 augmented images and corresponding masks. Images were captured using various smartphone cameras and stored in RGB color format in JPEG format. The captured images were labeled using the labelme tool to generate segmented masks. Subsequently, the dataset was augmented to generate the final dataset.

面向图像分割的多类别杂草数据集（Multiclass Weeds Dataset for Image Segmentation）包含两类杂草：裸柱菊（Soliva Sessilis，Field Burrweed）与菥蓂（Thlaspi Arvense L.，Field Pennycress）。该数据集的杂草图像采集自印度北方邦戈勒克布尔市的一处茄子种植农场，拍摄于杂草生长早期的田间环境中。数据集共包含7872张经过数据增强的图像及其对应的分割掩码：图像通过多款智能手机相机拍摄，采用RGB色彩格式存储，文件格式为JPEG。原始采集图像使用labelme标注工具进行标注，以生成分割掩码；随后通过数据增强操作生成了最终的数据集。

搜集汇总

背景与挑战

背景概述

该数据集包含7872张增强图像及对应掩码，涵盖两种杂草（Soliva Sessilis和Thlaspi Arvense L.）在茄子田早期生长阶段的图像，用于图像分割和深度学习研究。图像使用智能手机拍摄，并通过labelme工具标注，适用于农业工程和作物保护领域。