玉米幼苗与杂草数据集

该数据集是在新疆昌吉华兴农场玉米试验田采集，共有4130张图片、10521个边界框，用于玉米田真实场景杂草检测算法研究。反枝苋1304个实例、灰黎717个实例、龙葵290个实例、骆驼刺328个实例、马齿苋1390个实例、田旋花1227个实例、狗尾草891个实例、合被苋859个实例和玉米幼苗3515个实例。yolo类别标签如下：Amaranthus': 0, 'Chenopodium': 1, 'Solanum nigrum': 2, 'Alhagi sparsifolia': 3, 'Purslane': 4, 'Convolvulus': 5, 'Setaria': 6, 'Amaranthus polygonoides': 7, 'Corn': 8。现有的数据集多为单目标的图片，本次的数据集以多目标为主，弥补了杂草检测领域的数据集短缺的不足。

This dataset was collected from a corn experimental field at Huaxing Farm, Changji, Xinjiang Uygur Autonomous Region. It contains a total of 4130 images and 10521 bounding boxes, and is designed for research on weed detection algorithms in real-world cornfield scenarios. The dataset includes 1304 instances of Amaranthus, 717 instances of Chenopodium, 290 instances of Solanum nigrum, 328 instances of Alhagi sparsifolia, 1390 instances of Purslane, 1227 instances of Convolvulus, 891 instances of Setaria, 859 instances of Amaranthus polygonoides, and 3515 instances of corn seedlings. The YOLO category labels are as follows: 'Amaranthus': 0, 'Chenopodium': 1, 'Solanum nigrum': 2, 'Alhagi sparsifolia': 3, 'Purslane': 4, 'Convolvulus': 5, 'Setaria': 6, 'Amaranthus polygonoides': 7, 'Corn': 8. Most existing datasets primarily focus on single-target images, while this dataset mainly covers multiple target categories, which addresses the shortage of datasets in the field of weed detection research.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于玉米田杂草检测的图像数据集，采集自新疆昌吉华兴农场玉米试验田，包含4130张图片和10521个边界框，覆盖9个类别（包括玉米幼苗和8种杂草）。其特点是以多目标图像为主，旨在弥补杂草检测领域数据集的不足，支持真实场景下的算法研究。