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2022年黑龙江小麦、玉米、水稻苗期图像数据集

秦佳乐 苑江浩 宋国柱 姚鸿勋 郭雷风 王晓丽

农业大数据学报2024,Vol.6Issue(4):558-563,6.
农业大数据学报2024,Vol.6Issue(4):558-563,6.DOI:10.19788/j.issn.2096-6369.100026

2022年黑龙江小麦、玉米、水稻苗期图像数据集

Image Dataset of Wheat,Corn,and Rice Seedlings in Heilongjiang Province in 2022

秦佳乐 1苑江浩 2宋国柱 3姚鸿勋 4郭雷风 5王晓丽6

作者信息

  • 1. 山西农业大学软件学院,山西太谷 030801||中国农业科学院农业信息学研究所,北京 100081
  • 2. 国家粮食和物资储备局科学研究院,北京 100037
  • 3. 山西农业大学软件学院,山西太谷 030801
  • 4. 哈尔滨工业大学计算机科学与技术学院,哈尔滨 150001
  • 5. 中国农业科学院农业信息学研究所,北京 100081
  • 6. 中国农业科学院农业信息学研究所,北京 100081||国家农业科学数据中心,北京 100081||三亚中国农业科学院国家南繁研究院,海南三亚 572024
  • 折叠

摘要

Abstract

During the cultivation process,most field crops are typically grown in open fields.The northeastern region of China experiences relatively low temperatures throughout the year.During the seedling stage of crops,significant fluctuations in sunlight and rainfall can easily lead to issues such as weak and stunted seedlings,poorly developed root systems,and slow growth.Timely monitoring and management of crops during the seedling stage can help in understanding their growth status and environmental conditions,enabling prompt decision-making.Experimental data was collected from May 9,2022,to June 16,2022.RGB cameras installed at 11 meteorological stations in the experimental fields collected data seven times a day at 6:00,8:00,10:00,12:00,14:00,16:00,and 18:00.The images were captured at a height of 2.4 meters with a field of view angle of 90°,covering an area of 4.4 meters in length and 2.5 meters in width.Photography was mainly conducted through natural light conditions with a downward vertical perspective.After organizing and screening,the dataset comprises approximately 2.59 GB of data,including 1.48 GB of visible light RGB data and 1.11 GB of near-infrared spectral data.This dataset enables leaf age identification through visible light RGB data and near-infrared spectral data.Extracted features(color features,image features,texture features,vegetation indices)can be inputted into machine learning regression models for analysis and prediction.Moreover,this dataset is suitable for constructing convolutional neural network models for crop recognition or seedling identification,facilitating precise crop detection and further research on issues such as missed or replanted seedlings after transplanting.

关键词

黑龙江/苗期数据集/小麦/玉米/水稻/可见光图像/近红外光谱图像

Key words

Heilongjiang/seedling stage dataset/wheat/corn/rice/visible light image/near-infrared spectral image

引用本文复制引用

秦佳乐,苑江浩,宋国柱,姚鸿勋,郭雷风,王晓丽..2022年黑龙江小麦、玉米、水稻苗期图像数据集[J].农业大数据学报,2024,6(4):558-563,6.

基金项目

国家科技创新2030重大项目(2021ZD0110901) (2021ZD0110901)

内蒙古自治区科技计划项目(2021GG0341). (2021GG0341)

农业大数据学报

OACSTPCD

2096-6369

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