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GENERAL INFORMATION
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1. UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat


2. Author Information

        Name: Ali Moghimi
		
           Institution: University of Minnesota - Twin Cities
           Address: Department of Bioproducts and Biosystems Engineering, 1390 Eckles Ave, St. Paul, MN 55108, USA
           Email: moghi005@umn.edu

        Name: Ce Yang
           Institution: University of Minnesota - Twin Cities
           Address: Department of Bioproducts and Biosystems Engineering, 1390 Eckles Ave, St. Paul, MN 55108, USA
           Email: ceyang@umn.edu
  
        Name: James A. Anderson	   
			Institution: University of Minnesota - Twin Cities
			Address: Department of Agronomy & Plant Genetics, 991 Upper Buford Circle, St. Paul, MN 55108, USA
			Email: ander319@umn.edu


3. Date of data collection: Summer 2017 and summer 2018

4. Geographic location of data collection: 

	St. Paul Campus	Research Facility, (44°59′28.15″N and 93°10′48.34″W)
	University of Minnesota, MN 

5. Information about funding sources that supported the collection of the data:

	Minnesota’s Discovery, Research, and InnoVation Economy (MnDRIVE) program, the research area of Robotics, Sensors,
	and Advanced Manufacturing. 

	The graduate student fellowships provided by MnDRIVE Global Food Ventures and the department of
	Bioproducts and Biosystems Engineering.



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SHARING/ACCESS INFORMATION
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1. Licenses/restrictions placed on the data:

	Attribution 4.0 International (CC BY 4.0)
	https://creativecommons.org/licenses/by/4.0/
	

2. Links to publications that cite or use the data:

	https://doi.org/10.1016/j.compag.2020.105299
	
	https://arxiv.org/abs/1906.09666
	
	

3. Links to other publicly accessible locations of the data: N/A


4. Links/relationships to ancillary data sets: N/A


5. Was data derived from another source? N/A


6. Recommended citation for the data:

	Moghimi, Ali; Yang, Ce; Anderson, James A.(2020). UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat. 
	Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/0ch0-vb18.
	

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DATA & FILE OVERVIEW
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1. File List
   A. Filename: C3.zip; C4.zip; C9.zip
   
		Short description: 
						Aerial hyperspectral images of each plot in three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4).
						The name of the file is composed of field name (C3, C4, or C9) and plot ID.

		Further Notes:
						The files are 3-D matrices (x × y × λ) in which (x × y) denote the spatial information and
						λ refers to spectral information. 

		Files format:
						MATLAB m-files
			
		Open hyperspectral images:
						Programming language such as MATLAB, OCTAVE, and PYTHON

			
   B. Filename: C3_numpy.zip; C4_numpy.zip; C9_numpy.zip
   
		Short description: 
						Aerial hyperspectral images of each plot in three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4).
						The name of the file is composed of field name (C3, C4, or C9) and plot ID.

		Further Notes:
						The files are 3-D matrices (x × y × λ) in which (x × y) denote the spatial information and
						λ refers to spectral information. 

		Files format:
						Python numpy array
			
		Open hyperspectral images:
						Programming language such as PYTHON
						
						
	C. Filename: Yield_data.zip					
		
		Short description: 
						Ground truth data, which is the harvested yield of each plot in three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4).
						

		Further Notes:
						First column is plot ID and the second column is harvested yield in gram.  

		Files format:
						MATLAB m-files
						Python Pickle file
			
		Open hyperspectral images:
						Programming language such as PYTHON and MATLAB

2. Relationship between files: N/A      

3. Additional related data collected that was not included in the current data package: N/A

4. Are there multiple versions of the dataset? No


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METHODOLOGICAL INFORMATION
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1. Description of methods used for collection/generation of data: 

						Please refer to sections 2.1, 2.2, 2.3, and 2.4 in the following paper:
						
						https://doi.org/10.1016/j.compag.2020.105299
						
						or 
						
						https://arxiv.org/abs/1906.09666
						
						
2. Methods for processing the data:	

						Please read the following sections in the paper:
						
						2.5	Hyperspectral image analysis
						2.6	Dataset
						2.7	Deep Neural Network


3. Instrument- or software-specific information needed to interpret the data:

						Programming language such as MATLAB or PYTHON
			

4. Standards and calibration information, if appropriate:

						Please read the following sections in the paper:
						
						2.4.1	Radiometric Calibration
						
						2.4.2 	Noisy Band Removal


5. Environmental/experimental conditions:

						Please read the following section:

						2.1	Field site and experimental setup

6. Describe any quality-assurance procedures performed on the data: N/A


7. People involved with sample collection, processing, analysis and/or submission: N/A