2023年黑龙江水稻产量预测无人机遥感图像数据集

doi:10.19788/j.issn.2096-6369.100031

Abstract

Abstract:

Rice is one of the three major grain crops in China, and accurate, efficient and timely prediction of rice yield is crucial for variety selection and optimization of field management. UAV remote sensing system is widely used in crop pest and disease identification, crop growth monitoring and crop phenotyping by virtue of its advantages of fast, non-destructive, low cost and high throughput. To explore the role of spectral data in estimating rice yield, this dataset used UAV remote sensing to collect multispectral images of rice growth process, 106 sample points of 1 m×1 m were selected for manual sampling and yield measurement, and at the same time, visible images were collected after the sampling to realize the correlation between spectral images and yield data. The dataset of this paper was constructed after manual checking and organizing. The data collection location was Heilongjiang Province, and the UAV collected the data under cloudless and light-sufficient conditions, and the collection time was from July to August in 2023, and a total of 3 days of multispectral data and 1 day of visible light data were collected with different varieties in the experimental field. The dataset in this paper was complete in all data and provided data support for research on yield estimation.

Data summary：

Items	Description
Dataset name	Rice Yield Prediction UAV Remote Sensing Image Dataset of Heilongjiang Province in 2023
Specific subject area	Agricultural Science
Research Topic	computer vision
Time range	July 2023- August 2023
Temporal resolution	Day
Data types and technical formats	.tif,.xlsx,.jpg
Dataset structure	The dataset consists of three parts of data. The first part is the multispectral image data of the entire growth period of rice, including six spectral channels: blue (450nm), green (555nm), red (660nm), red edge 1 (720nm), red edge 2 (750nm), and near-infrared (840nm), with a total of 14226 images, approximately 32.6GB; The second part is production data, saved in. xlsx format; The third part is visible light image data used to annotate sampling points, totaling 746 images, approximately 18.9GB.
Volume of dataset	51.5 GB
Key index in dataset	Gradient settings, plot labeling, yield, multispectral images, RGB images
Data accessibility	CSTR：https://cstr.cn/17058.11.sciencedb.agriculture.00131 DOI：https://doi.org/10.57760/sciencedb.agriculture.00131 NASDC Access link： https://agri.scidb.cn/, restricted access
Financial support	National Science and Technology Major Project（2021ZD0110901）

Key words: unmanned aerial vehicle (UAV), rice, multispectral imagery, Heilongjiang

YUAN JiangHao, ZHENG ZuoJun, CHU ChangMing, YAO HongXun, LIU HaiLong, GUO LeiFeng. Rice Yield Prediction UAV Remote Sensing Image Dataset of Heilongjiang Province in 2023[J].Journal of Agricultural Big Data, 2024, 6(4): 546-551.

Figures/Tables 6

Table 1

Fig. 1

Fig. 2

Table 2

Fig. 3

Fig. 4

References 10

[1]	YANG S, HU L, WU H, et al. Estimation Model of Winter Wheat Yield Based on Uav Hyperspectral Data[C]// 2019 IEEE International Geoscience and Remote Sensing Symposium. Japan, 2019. https://doi.org/10.1109/IGARSS.2019.8898839.
[2]	陶惠林, 冯海宽, 杨贵军, 等. 基于无人机数码影像和高光谱数据的冬小麦产量估算对比[J]. 农业工程学报, 2019, 35(23):111-118.
[3]	TANAKA Y, WATANABE T, KATSURA K, et al. Deep learning enables instant and versatile estimation of rice yield using Ground- based RGB images[J]. Plant Phenomics, 2023, 5: 0073. https://doi.org/10.34133/plantphenomics.0073.
[4]	YANG W, NIGON T, HAO Z, et al. Estimation of corn yield based on hyperspectral imagery and convolutional neural network[J]. Computers and Electronics in Agriculture, 2021, 184: 106092. https://doi.org/10.1016/j.compag.2021.106092.
[5]	FEI S, HASSAN M A, MA Y, et al. Entropy Weight Ensemble Framework for yield prediction of winter wheat under different water stress treatments using unmanned aerial vehicle-based multispectral and thermal data[J]. Frontiers in Plant Science, Frontiers, 2021, 12: 730181. https://doi.org/10.3389/fpls.2021.730181.
[6]	杨俊, 丁峰, 陈晨, 等. 小麦生物量及产量与无人机图像特征参数的相关性[J]. 农业工程学报, 2019, 35(23):104-110.
[7]	MARQUES RAMOS A P, PRADO OSCO L, ELIS GARCIA FURUYA DL, et al. A Random Forest Ranking Approach to predict yield in maize with UAV-based Begetation Spectral Indices[J]. Computers and Electronics in Agriculture, 2020, 178: 105791. https://doi.org/10.1016/j.compag.2020.105791.
[8]	FU Z, JIANG J, GAO Y, et al. Wheat growth monitoring and yield estimation based on Multi-Rotor Unmanned Aerial Vehicle[J]. Remote Sensing, MDPI AG, 2020, 12(3): 508. https://doi.org/10.3390/rs12030508.
[9]	程千, 徐洪刚, 曹引波, 等. 基于无人机多时相植被指数的冬小麦产量估测[J]. 农业机械学报, 2021, 52(3):160-167.
[10]	朱婉雪, 李仕冀, 张旭博, 等. 基于无人机遥感植被指数优选的田块尺度冬小麦估产[J]. 农业工程学报, 2018, 34(11):78-86.

光谱通道	波长（nm）	标定板反射率
Blue	450	0.63
Green	555	0.61
Red	660	0.60
Rededge	720	0.60
Rededge-750	750	0.60
Nir	840	0.58

数据项	计量单位	数据说明
地块标号	/	各地块标号顺序参照图2
梯度设置	/	包括4个品种
产量	kg	1 m×1 m样本点产量

[1]	QIN JiaLe, YUAN JiangHao, SONG GuoZhu, YAO HongXun, GUO LeiFeng, WANG XiaoLi. Image Dataset of Wheat, Corn, and Rice Seedlings in Heilongjiang Province in 2022 [J]. Journal of Agricultural Big Data, 2024, 6(4): 558-563.
[2]	DU JiaKuan, LI YanFei, SUN SiWen, LIU JiDong, JIANG TengDa. Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion [J]. Journal of Agricultural Big Data, 2024, 6(1): 56-67.
[3]	ZHAN ZiSen, ZHANG XiaoHeng, CHEN Bo. A Dataset on the Compiled Materials of Costs and Profits of Rice, Wheat and Corn Products of China [J]. Journal of Agricultural Big Data, 2023, 5(4): 110-117.
[4]	ZHANG MengMeng, WANG XiuJuan, KANG MengZhen, HUA Jing, WANG HaoYu, WANG FeiYue. A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification [J]. Journal of Agricultural Big Data, 2023, 5(4): 13-23.
[5]	WEI TongYang, XU Ke, XU Lei. Dataset for Analyzing the Horizontal Transmission Mechanism of Domestic Financial Markets to Agricultural Commodity Prices, 2017-2021 [J]. Journal of Agricultural Big Data, 2023, 5(3): 19-25.
[6]	KONG FanTao, AN Min, SUN Wei, LIU JiFang, HU Lin, CAO ShanShan. A Dataset of the Statistics on Tomato Transaction Price in the Wholesale Market in Beijing-Tianjin-Hebei Region from 2012 to 2019 [J]. Journal of Agricultural Big Data, 2023, 5(2): 62-67.
[7]	ZHANG HaiFeng, ZHANG Yu, LAI YongCai, ZHENG YanYan, LIU Kai, BI HongWen. Construction and Application of Monitoring Platform for Saline-alkali Land in Cold Regions [J]. Journal of Agricultural Big Data, 2023, 5(2): 122-131.
[8]	WANG BoYuan, GUAN ZhiHao, YANG Yang, HU Lin, WANG XiaoLi. The Agricultural Pest and Disease Image Recognition Dataset in Nanjing, Jiangsu Province, in 2023 [J]. Journal of Agricultural Big Data, 2023, 5(2): 91-96.
[9]	WANG XueYing, CHEN XianGuan, TANG ShunJie, FENG LiPing. Sensitivity Analysis and Adaptability Evaluation of RiceSM Model [J]. Journal of Agricultural Big Data, 2023, 5(2): 97-108.
[10]	GUAN Bolun, DONG Wei, ZHANG Liping, YANG Qianjin, WANG Yan. Development and Application of Traceability Tracking Platform for Ratooning Rice [J]. Journal of Agricultural Big Data, 2023, 5(1): 55-67.
[11]	JIN Yuze, JIA Xinwei, LAI Wangfeng, ZHOU Hongli, CHEN Naihe, LI Tao. Exploration and Application of Big Data Technology in Pork Price Prediction and Regulation [J]. Journal of Agricultural Big Data, 2023, 5(1): 126-134.
[12]	Jia He, Laigang Wang, Yan Guo, Yan Zhang, Xiuzhong Yang, Ting Liu, Hongli Zhang. Estimating the Leaf Area Index of Maize based on Unmanned Aerial Vehicle Multispectral Remote Sensing [J]. Journal of Agricultural Big Data, 2021, 3(4): 20-28.
[13]	Ayitula Maimaitizunong, Shuai Yanju, Haodong Wei, Zhen He, Qinxi Xiao, Qiong Hu, Baodong Xu, Liangzhi You, Cougui Cao, Lin Ling. Evaluation of Green Development of Rice-Based Cropping Systems Using Remote Sensing Data and the DNDC Model: Case Study of Qianjiang City [J]. Journal of Agricultural Big Data, 2021, 3(3): 33-44.
[14]	Yijun Meng, Xiaolei Qiu, Leilei Liu, Bing Liu, Yan Zhu, Weixing Cao, Liang Tang. Sensitivity Analysis of Genetic Parameters of RiceGrow Model [J]. Journal of Agricultural Big Data, 2021, 3(3): 23-32.
[15]	Xiaoyan Zhang, Li Meng, Lili Wang, Feng Liu, Jiajia Liu, Decheng Lu. Analysis of the Price Fluctuation Characteristics of China’s Wheat Market Using Big Data: A Case Study of Shandong Province [J]. Journal of Agricultural Big Data, 2020, 2(3): 75-83.

Rice Yield Prediction UAV Remote Sensing Image Dataset of Heilongjiang Province in 2023

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 10

Related Articles 15

Metrics

Comments

Recommended 0