数据论文

2022年内蒙古无人机马铃薯图像数据集

展开
  • 1.新疆农业大学计算机与信息工程学院,乌鲁木齐 830052
    2.中国农业科学院农业信息研究所,北京 100081
    3.内蒙古科学技术研究院,呼和浩特 010010
    4.国家农业科学数据中心,北京 100081
胡天赐,男,在读硕士,研究方向:农业信息化方向;E-mail: 1272341570@qq.com

收稿日期: 2023-03-06

  网络出版日期: 2023-05-16

基金资助

内蒙古自治区科技计划项目(2021GG0341);国家科技创新2030重大项目(2021ZD0110901);国家重点研发计划(2022YFF0712100);中国农业科学院院级基本科研业务费(Y2022LM20)

2022 Inner Mongolia UAV Potato Image Dataset

Expand
  • 1. College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    2. Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3. Inner Mongolia Academy of Science and Technology, Hohhot 010010, China
    4. National Agriculture Science Data Center, Beijing 100081, China

Received date: 2023-03-06

  Online published: 2023-05-16

摘要

马铃薯是世界第四大粮食作物,规模化种植是其高产量的基础性保障。随着数字农业的发展,马铃薯的规模化种植方式也日益趋向自动化与智能化。无人机是作物植保和生长监测的重要工具,无人机光谱数据在作物识别、作物生长状况分析等方面发挥重要的作用。为探究光谱数据及图像数据在马铃薯生长中所发挥的作用,文章利用无人机遥感获取不同高度的多光谱影像,并对地面的马铃薯叶片数据进行采集,经过人工检查和整理构建了论文数据集。数据采集地点位于内蒙古呼伦贝尔两块成熟期种薯试验田,采集时间为2022年8月13日、16日和18日,期间共完成了3次不同空间分辨率的光谱数据及图像数据采集。本文数据集的光谱数据完整,叶片数据清晰,可为马铃薯作物识别、种植面积估测以及成熟期期间不同日期的马铃薯相关植被指数变化等研究提供数据支撑。

本文引用格式

胡天赐, 王瑞利, 蒋呈祥, 白涛, 胡林, 王晓丽, 郭雷风 . 2022年内蒙古无人机马铃薯图像数据集[J]. 农业大数据学报, 2023 , 5(1) : 40 -45 . DOI: 10.19788/j.issn.2096-6369.230112

Abstract

Potatoes are the fourth largest food crop in the world, and large-scale planting of potatoes is an important basis for ensuring high yields of potatoes. With the development of digital agriculture, the large-scale planting of potatoes also tends to be automated and intelligent. UAVs are an important tool in crop plant protection and growth monitoring. UAV spectral data play an important role in crop identification and crop growth status analysis. important. In order to explore the role of spectral data and image data in potato growth, this study conducted three different spatial resolution images on two mature seed potato experimental fields in Hulunbeier, Inner Mongolia, on August 13, 16 and 18, 2022. Spectral data and image data are collected. UAV remote sensing was used to obtain multi- spectral images at different heights, and the data of potato leaves on the ground were collected. After manual in- spection and sorting, this dataset was constructed. The spectral data of this dataset is complete and the leaf data is clear, which can provide data support for research on potato crop identification, planting area estimation, and potato-related vegetation index changes on different dates during the maturity period.

参考文献

[1] 谢从华. 马铃薯产业的现状与发展[J]. 华中农业大学学报(社会科学版), 2012(1): 1-4.
[1] Xie C H. Current situation and development of potato industry[J]. Journal of Huazhong Agricultural University (Social Science Edition), 2012(1): 1-4.
[2] 刘鑫, 冯洁, 杨舒明. 马铃薯叶片晚疫病的多光谱分类识别[J]. 光学仪器, 2017, 39(1): 11-17.
[2] Liu X, Feng J, Yang S M. Multispectral classification and identification of potato leaf blight[J]. Optical Instruments, 2017, 39(1): 11-17.
[3] 党满意, 孟庆魁, 谷芳, 等. 基于机器视觉的马铃薯晚疫病快速识别[J]. 农业工程学报, 2020, 36(2): 193-200.
[3] Dang M Y, Meng Q K, Gu F, et al. Rapid identification of potato late blight based on machine vision[J]. Journal of Agricultural Engineering, 2020, 36(2): 193-200.
[4] 陈鹏, 冯海宽, 李长春, 等. 无人机影像光谱和纹理融合信息估算马铃薯叶片叶绿素含量[J]. 农业工程学报, 2019, 35(11): 63-74.
[4] Chen P, Feng H K, Li C C, et al. Estimation of chlorophyll content in potato leaves by fusion of spectrum and texture information from UAV images[J]. Journal of Agricultural Engineering, 2019, 35(11): 63-74.
[5] 杨海波, 李渊, 尹航, 等. 基于新组合光谱指数的马铃薯植株氮含量遥感估测[J]. 土壤, 2022, 54(2): 385-395.
[5] Yang H B, Li Y, Yin H, et al. Remote sensing estimation of nitrogen content in potato plants based on a new combined spectral index[J]. Soil, 2022, 54(2): 385-395.
[6] 王来刚, 贺佳, 郑国清, 郭燕, 张彦, 张红利. 基于无人机多光谱遥感的玉米FPAR估算[J]. 农业机械学报, 2022, 53(10): 202-210.
[6] Wang L G, He J, Zheng G Q, et al. UAV-based multispectral remote sensing for FPAR estimation of maize[J]. Journal of Agricultural Machinery, 2022, 53(10): 202-210.
[7] 刘杨, 冯海宽, 黄珏, 等. 基于无人机数码影像的马铃薯生物量估算[J]. 农业工程学报, 2020, 36(23): 181-192.
[7] Liu Y, Feng H K, Huang J, et al. Potato biomass estimation based on UAV digital image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(23): 181-192.
[8] 刘杨, 冯海宽, 孙乾, 等. 不同分辨率无人机数码影像的马铃薯地上生物量估算研究[J]. 光谱学与光谱分析, 2021, 41(5): 1470-1476.
[8] Liu Y, Feng H K, Sun Q, et al. Estimation of potato aboveground biomass based on UAV digital images with different resolutions[J]. Spectroscopy and Spectral Analysis, 2021, 41(5): 1470-1476.
文章导航

/