Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean
Abstract
:1. Introduction
- Asses yield prediction of snap bean, at various points during the growing season, using hyperspectral imagery and descriptive models;
- Identify discriminating spectral features explaining yield; and
- Evaluate the most accurate time (growth period) for yield prediction, prior to harvest.
2. Methods
2.1. Study Area
2.2. Assessment of Plant Growth Characteristics
2.3. Data Collection
2.4. Data Preprocessing
2.4.1. Calibration to Reflectance
2.4.2. Vegetation Detection
2.4.3. Spectral Denoising
2.5. Data Analysis
2.5.1. Jostar: Feature Selection Library in Python
2.5.2. Feature Selection Procedure
2.6. Software
3. Results
3.1. Descriptive Statistics
3.2. Pod Weight
3.2.1. 2019 Data Set
3.2.2. 2020 Data Set
3.3. Seed Length
3.3.1. 2019 Data Set
3.3.2. 2020 Data Set
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACO | ant colony optimization |
DAP | days after planting |
ELM | empirical line method |
FWHM | full-width-half-maximum |
GA | genetic algorithm |
GDP | gross domestic product |
GPU | graphics processing unit |
GSD | ground sampling distance |
KNN | K-nearest neighbors |
LiDAR | light detection and ranging |
LRS | plus-L minus-R |
MAP | minimum average partial |
NIR | near infrared |
NSGAII | non-dominated sorting genetic algorithm-II |
PA | precision agriculture |
PC | principal component (PC) |
PCA | principal component analysis |
PLSR | partial least squares regression |
PSO | particle Swarm Optimization |
coefficient or determination | |
RE | red edge |
RENDVI | red-edge normalized difference vegetation index |
RF | random forests |
RFE | recursive feature elimination |
root mean square error | |
RS | remote sensing |
SA | simulated annealing |
SAM | spectral angle mapper |
SBS | sequential backward search |
SFS | sequential forward search |
SVR | support vector regression |
UAS | unmanned aerial systems |
VIs | vegetation indices |
VIS | visible |
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Model | Date | Stage | Days after Planting (DAP) | Heat Unit () * | GSD (cm) |
---|---|---|---|---|---|
2019 | 06/27/2019 | Sowing | 0 | 0 | N/A |
08/01/2019 | Flowering | 35 | 794 | 3 | |
08/05/2019 | Flowering | 39 | 865 | 3 | |
08/12/2019 | Pod formation | 46 | 997 | 1.5 | |
08/14/2019 | Pod formation | 48 | 1040 | 3 | |
08/16/2019 | Pod formation | 50 | 1074 | 3 | |
08/20/2019 | Pod formation (early harvest) | 54 | 1158 | 3 | |
08/22/2019 | Pod formation (late harvest) | 56 | 1205 | 3 | |
2020 | 06/27/2020 | Sowing | 0 | 0 | N/A |
07/28/2020 | Budding | 31 | 760 | 3 | |
07/31/2020 | Flowering | 34 | 822 | 3 | |
08/06/2020 | Flowering | 40 | 945 | 3 | |
08/10/2020 | Pod formation | 44 | 1022 | 3 | |
08/14/2020 | Pod formation | 48 | 1116 | 3 | |
08/21/2020 | Pod formation | 55 | 1239 | 3 | |
08/24/2020 | Pod formation (early harvest) | 58 | 1306 | 3 | |
08/26/2020 | Pod formation (late harvest) | 60 | 1346 | 3 |
Optimization Model | Parameter | Description | Sampling Method | Low Bound | High Bound |
---|---|---|---|---|---|
GA | Crossover percentage | Uniform logarithmic | 0.5 | ||
Mutation percentage | Uniform logarithmic | 0.2 | |||
Mutation rate | Uniform logarithmic | 0.2 | |||
Selection pressure | Random integer | 1 | 10 | ||
Population size | Random integer | 20 | 200 | ||
SA | Cooling factor | Uniform logarithmic | 0.8 | 0.99 | |
Initial temperature | Uniform logarithmic | 500 | |||
Number of sub-iterations | Random Integer | 20 | 200 | ||
PSO | Information elicitation factor | Uniform logarithmic | 0.5 | ||
Cognitive parameter | Uniform logarithmic | 2 | |||
Social parameter | Uniform logarithmic | 2 | |||
W | Inertia weight | Uniform logarithmic | 1.2 | ||
Inertia weight damping factor | Uniform logarithmic | 0.5 | |||
Number of particles | Random integer | 20 | 200 | ||
ACO | Information elicitation factor | Uniform logarithmic | 0.5 | ||
Pheromone evaporation coefficient | Uniform logarithmic | 0.5 | |||
Initial pheromone intensity | Uniform logarithmic | 1 | |||
Q | Pheromone intensity | Uniform logarithmic | 1 | ||
Number of ants | Random integer | 20 | 200 | ||
Meta-heuristic factor | Random integer | 1 | 5 |
Data Set | Yield Indicator | Across Yield Indicator WL (nm) Similarity | |||||
---|---|---|---|---|---|---|---|
Pod Weight | Seed Length | ||||||
Zone (DAP) | WL (nm) | Dip | Zone (DAP) | WL (nm) | Dip | ||
2019 early | Blue: E. 50 Green: T. Red: E. 39, 46 RE: E. 48, 54 NIR: NA | 3x: 451 2x: 478, 505, 516, 525 | at 39 DAP None | Blue: E. 39, 48 Green: E. 50, 54 Red: T. RE: E. 35, 48 NIR: O. e 50, 54 | 2x: 451, 585, 659, 710, 716 | at 40 DAP at 55 DAP | 451 |
2019 late | Blue: E. 35, 50 Green: T. Red: E. 39 RE: E. 46, 54, 56 NIR: O. 35, 56 | 4x: 525 3x: 451 2x: 505, 541, 659, 759, 819 | None at 56 DAP | Blue: E. 35, 50 Green: E. 54, 56 Red: E. 54 RE: O. 48 NIR: O. 54 | 3x: 451, 721 2x: 583, 585, 659, 716 | at 46 DAP at 56 DAP | 451, 659 |
2020 early | Blue: T. Green: E. 44 Red: E. 58 RE: T. NIR: NA | 5x: 451, 759 3x: 523 2x: 518, 607, 647 | at 34 DAP at 55 DAP | Blue: T. Green: E. 58 Red: E. 44, 55, 58 RE: E. 31, 48 NIR: O. 58 | 6x: 451 2x: 494, 518 | at 44 DAP None | 451, 518 |
2020 late | Blue: E. 58 Green: E. 44 Red: E. 40 RE: E. 31, 55, 60 NIR: NA | 4x: 451, 756 2x: 474, 500, 525, 654, 707 | at 34 DAP at 60 DAP | Blue: T. Green: E. 58 Red: E. 58, 60 RE: E. 31, 34 NIR: O. 58 | 7x: 451 2x: 494, 518, 670, 699, 759 | at 44 DAP at 60 DAP | 451, ∼500, ∼520, ∼700, ∼760 |
Across years WL (nm) similarity | 451, ∼500, ∼ 520, ∼650, ∼760 | 451, ∼520, ∼500, ∼585, ∼660, ∼720 | ∼451, ∼500, ∼520, ∼650, ∼700, ∼760 |
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Hassanzadeh, A.; Zhang, F.; van Aardt, J.; Murphy, S.P.; Pethybridge, S.J. Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean. Remote Sens. 2021, 13, 3241. https://doi.org/10.3390/rs13163241
Hassanzadeh A, Zhang F, van Aardt J, Murphy SP, Pethybridge SJ. Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean. Remote Sensing. 2021; 13(16):3241. https://doi.org/10.3390/rs13163241
Chicago/Turabian StyleHassanzadeh, Amirhossein, Fei Zhang, Jan van Aardt, Sean P. Murphy, and Sarah J. Pethybridge. 2021. "Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean" Remote Sensing 13, no. 16: 3241. https://doi.org/10.3390/rs13163241