Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Control Point Establishment and Image Acquisition
2.3. Image Pre-Processing
2.4. Strawberry Yield, and Flower and Fruit Count Data Collection
2.5. Weather Variables
2.6. Canopy Size Variables Extraction
2.7. Statistical Analysis Methods
3. Results
3.1. Image-Derived and Field-Observed Flower and Fruit Counts
3.2. Yield Prediction Based on Imagery, Weather, and Canopy Characteristics
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Weather Variables | Variable Description |
---|---|
Temperature—Air (60 cm) | Average daily temperature from a probe 60 cm above ground (°C) |
Temperature—Air (2 m) | Average daily temperature from a probe 2 m above ground (°C) |
Temperature—Air (10 m) | Average daily temperature from a probe 10 m above ground (°C) |
Soil temperature | Average daily temperature from a probe 4 inches below ground (°C) |
Relative humidity | Average percentage of saturation of a specific volume of air (%) |
Rainfall | Total rainfall (inches) |
Barometric pressure | Pressure within the atmosphere of Earth (millibars) |
Solar radiation | Radiant energy emitted by the sun (watts per square meter) |
Wind speed | Average wind speed (miles per hour) |
Wind direction | Direction from which the wind blows (degrees) |
Dew point temperature | Temperature at which dew starts to form on solid surfaces (°C) |
Evapotranspiration | Sum of evaporation and transpiration (inches per day) |
Canopy Size Variables | Variable Description |
---|---|
Canopy Average Height | Average canopy height within each plot |
Canopy Height Standard Deviation (std) | Standard deviation of canopy height within each plot |
Canopy Area | Canopy planimetric area within each plot |
Canopy Volume | Canopy volume computed from canopy heights and summarized at the plot level |
a. | Flower Count Prediction | |||
Optimal Prediction Model | RMSE of Prediction in Flower Count per Plot | Goodness of Fit | ||
Model 1.a | 105 | 88.2% | ||
b. | Fruit Count Prediction | |||
Optimal Prediction Model | RMSE of Prediction in Fruit Count per Plot | Goodness of Fit | ||
Model 1.b | 268 | 92.6% |
a. | Yield Prediction at 3–4 Days Ahead of Harvest | |||
Optimal Prediction Model | RMSE of Predictioin in Flat (8lb) per Acre | Goodness of Fit | ||
Model 2.a | 1222 | 75.1% | ||
Model 3.a | 1172 | 79.7% | ||
Model 4.a | 866 | 83.4% | ||
b. | Yield Prediction at 1 Week Ahead of Harvest | |||
Optimal Prediction Model | RMSE of Prediction in Flat (8lb) per Acre | Goodness of Fit | ||
Model 2.b | 1362 | 88.3% | ||
Model 3.b | 1307 | 89.9% | ||
Model 4.b | 1122 | 92.1% | ||
c. | Yield Prediction at 3 Weeks Ahead of Harvest | |||
Optimal Prediction Model | RMSE of Prediction in Flat (8lb) per Acre | Goodness of Fit | ||
Model 2.c | 1178 | 95.4% | ||
Model 3.c | 1193 | 95.6% | ||
Model 4.c | 1055 | 96.7% |
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Abd-Elrahman, A.; Wu, F.; Agehara, S.; Britt, K. Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches. ISPRS Int. J. Geo-Inf. 2021, 10, 239. https://doi.org/10.3390/ijgi10040239
Abd-Elrahman A, Wu F, Agehara S, Britt K. Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches. ISPRS International Journal of Geo-Information. 2021; 10(4):239. https://doi.org/10.3390/ijgi10040239
Chicago/Turabian StyleAbd-Elrahman, Amr, Feng Wu, Shinsuke Agehara, and Katie Britt. 2021. "Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches" ISPRS International Journal of Geo-Information 10, no. 4: 239. https://doi.org/10.3390/ijgi10040239