Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Description
2.1.1. Agronomical Data
2.1.2. BBCH-Scale
- (1)
- BBCH 0–60 (from dormancy to the beginning of flowering)
- (2)
- BBCH 61–70 (from the beginning of flowering to the end of flowering
- (3)
- BBCH > 70 (from the beginning of fruit growth to harvest)
2.1.3. Numerical Features
- (1)
- Air temperature (avg.) [°C]
- (2)
- Air temperature (max.) [°C]
- (3)
- Air temperature (min.) [°C]
- (4)
- Rainfall [mm]
- (5)
- Air relative humidity (avg.) [%]
- (6)
- Air relative humidity (max.) [%]
- (7)
- Air relative humidity (min.) [%]
- (8)
- Dew point temperature (avg.) [°C]
- (9)
- Dew point temperature (max.) [°C]
- (10)
- Dew point temperature (min.) [°C]
- (1)
- Irrigation [kg]
- (2)
- Fertigation [l]
- (1)
- pH
- (2)
- S-SO—sulfur [mg/L]
- (3)
- P—phosphorus [mg/L]
- (4)
- K—potassium [mg/L]
- (5)
- C—calcium [mg/L]
- (6)
- Mg—magnesium [mg/L]
- (7)
- Fe—iron [mg/L]
- (8)
- Zn—zinc [mg/L]
- (9)
- Mn—manganese [mg/L]
- (10)
- Cu—copper [mg/L]
- (11)
- B—boron [mg/L]
- (12)
- Cl—chlorine [mg/L]
- (13)
- Na—sodium [mg/L]
- (14)
- N-NO—nitrogen [mg/L]
2.1.4. BBCH Soil and Climate Features
- (1)
- Insolation (BBCH 0–60) [W/m]
- (2)
- Insolation (BBCH 61–70) [W/m]
- (3)
- Insolation (BBCH > 70) [W/m]
- (4)
- Rainfall (BBCH 0–60) [mm]
- (5)
- Rainfall (BBCH 61–70) [mm]
- (6)
- Rainfall (BBCH > 70) [mm]
- (7)
- Irrigation (BBCH 0–60) [mm]
- (8)
- Irrigation (BBCH 61–70) [mm]
- (9)
- Irrigation (BBCH > 70) [mm]
- (10)
- Daily air temperature (avg.) (BBCH 0–60) [°C]
- (11)
- Daily air temperature (avg.) (BBCH 61–70) [°C]
- (12)
- Daily air temperature (avg.) (BBCH > 70) [°C]
- (13)
- Daily soil temperature (avg.) (BBCH 0–60) [°C]
- (14)
- Daily soil temperature (avg.) (BBCH 61–70) [°C]
- (15)
- Daily soil temperature (avg.) (BBCH > 70) [°C]
- (16)
- Soil pH (avg.) (BBCH 0–60)
- (17)
- Soil pH (avg.) (BBCH 61–70)
- (18)
- Soil pH (avg.) (BBCH > 70)
- (19)
- Soil humidity (avg.) (BBCH 0–60) [%]
- (20)
- Soil humidity (avg.) (BBCH 61–70) [%]
- (21)
- Soil humidity (avg.) (BBCH > 70) [%]
- (22)
- Soil P—phosphorus (avg.) (BBCH 0–60) [mg/L]
- (23)
- Soil P—phosphorus (avg.) (BBCH 61–70) [mg/L]
- (24)
- Soil P—phosphorus (avg.) (BBCH > 70) [mg/L]
- (25)
- Soil Mg—magnesium (avg.) (BBCH 0–60) [mg/L]
- (26)
- Soil Mg—magnesium (avg.) (BBCH 61–70) [mg/L]
- (27)
- Soil Mg—magnesium (avg.) (BBCH > 70) [mg/L]
- (28)
- Soil K—potassium (avg.) (BBCH 0–60) [mg/L]
- (29)
- Soil K—potassium (avg.) (BBCH 61–70) [mg/L]
- (30)
- Soil K—potassium (avg.) (BBCH > 70) [mg/L]
2.1.5. Vegetation Features
- (1)
- EVI—Enhanced Vegetation Index
- (2)
- NDVI—Normalized Difference Vegetation Index
- (3)
- RDVI—Renormalized Difference Vegetation Index
- (4)
- SAVI—Soil-Adjusted Vegetation Index
- (1)
- EVI 40 days before harvest (max.)
- (2)
- EVI 40 days before harvest (avg.)
- (3)
- EVI 40 days before harvest (min.)
- (4)
- EVI 40 days before harvest (stddev.)
- (5)
- NDVI 40 days before harvest (max.)
- (6)
- NDVI 40 days before harvest (avg.)
- (7)
- NDVI 40 days before harvest (min.)
- (8)
- NDVI 40 days before harvest (stddev.)
- (9)
- RDVI 40 days before harvest (max.)
- (10)
- RDVI 40 days before harvest (avg.)
- (11)
- RDVI 40 days before harvest (min.)
- (12)
- RDVI 40 days before harvest (stddev.)
- (13)
- SAVI 40 days before harvest (max.)
- (14)
- SAVI 40 days before harvest (avg.)
- (15)
- SAVI 40 days before harvest (min.)
- (16)
- SAVI 40 days before harvest (stddev.)
2.1.6. Selyaninov Hydrothermal Coefficient
- (1)
- HTC (BBCH 0–60)
- (2)
- HTC (BBCH 61–70)
- (3)
- HTC (BBCH > 70)
2.1.7. GDD Features
- (1)
- GDD (BBCH 0–60)
- (2)
- GDD (BBCH 61–70)
- (3)
- GDD (BBCH > 70)
2.1.8. Aggregates Based on Mineral Fertilization and Fertigation
- (1)
- Fertilization (BBCH 0–60) [kg]
- (2)
- Fertilization (BBCH 61–70) [kg]
- (3)
- Fertilization (BBCH > 70) [kg]
- (4)
- Fertigation (BBCH 0–60) [l]
- (5)
- Fertigation (BBCH 61–70) [l]
- (6)
- Fertigation (BBCH > 70) [l]
- (7)
- K—potassium-Fertilization (annually) [kg]
- (8)
- N—nitrogen-Fertilization (annually) [kg]
- (9)
- P—phosphorus-Fertilization (annually) [kg]
2.1.9. Harmful Features
- (1)
- hailstorm percentage of damage [%]
- (2)
- hailstorm cut fruit [%]
2.1.10. Features Summary
2.2. Data Preprocessing Methods
2.2.1. Data Normalization
- (1)
- Crop/Harvest
- (2)
- Irrigation
- (3)
- Fertigation
- (1)
- hailstorm percentage of damage [%]
- (2)
- hailstorm cut fruit [%]
2.2.2. Finding and Replacing Missing Values
- (1)
- Imputation Using (Mean/Median) Values
- (2)
- Imputation Using (Most Frequent) or (Zero/Constant) Values
- (3)
- Stochastic Regression Imputation
- (4)
- Extrapolation and Interpolation
- (5)
- Imputation Using k-NN
- (6)
- Imputation Using XGBoost
- (7)
- Others
- (1)
- S-SO—sulfur
- (2)
- Cl—chlorine
- (3)
- Fertigation (BBCH 0–60)
- (4)
- Fertilization (BBCH 0–60)
- (5)
- Hailstorm percentage of damage
- (6)
- Hailstorm cut fruit
Extreme Gradient Boosting-XGBoost
Algorithm 1: Algorithm of missing value imputation using XGBoost |
procedure FillMissingByXGBoost() ▹ do not touch real value of features return end procedure |
2.3. Feature Generation Using PCA (Principal Component Analysis) Method
2.4. Outlier Detection
2.4.1. Local Outlier Factor (LOF)
2.4.2. Unsupervised Outlier Detection Based on OneClassSVM
2.5. Feature Selection
2.5.1. Stepwise Regression
- (1)
- Fit the initial model.
- (2)
- If any features not in the model have a p-value less than the input tolerance (e.g., 0.05), add the one with the smallest p-value and repeat this step. For example, suppose the initial model is the default model and the input tolerance = 0.05. The algorithm first fits all models consisting of the constant plus the first feature and looks for the next feature that has the smallest p-value, for example feature 4. If feature 4’s p-value is less than 0.05 then feature 4 is added to the model. Then the algorithm searches among all models consisting of the constant + feature 4 and looks at the next features. If the trait not in the model has a p-value less than 0.05, the trait with the smallest p-value is added to the model and the process is repeated. When there are no further features to add to the model, the algorithm moves to step 3.
- (3)
- If any features in the model have a p-value greater than the output tolerance -premove (e.g., 0.06), remove those with the largest p-value and go to step 2; otherwise the algorithm will finish computations and return the resulting feature list.
2.5.2. Pearson’s Feature Selection Method
2.5.3. Chi-square Feature Selection Method
2.6. Prediction Methods Applied
2.6.1. Linear Regression
2.6.2. Ridge
2.6.3. Lasso
2.6.4. ElasticNet
2.6.5. Random Forest Regressor
2.6.6. MLP Regressor
2.6.7. SGD Regressor
2.6.8. SVR and NuSVR
3. Results and Discussion
Algorithm 2: Algorithm of numerical experiments |
- (1)
- Fertigation
- (2)
- Hailstorm percentage of damage
- (3)
- EVI 40 days before harvest (avg.)
- (4)
- RDVI 40 days before harvest (max.)
- (5)
- Dew point temperature (max.)
- (6)
- NDVI 40 days before harvest (avg.)
- (7)
- SAVI 40 days before harvest (min.)
- (8)
- SAVI 40 days before harvest (stddev.)
- (9)
- Irrigation (BBCH > 70)
- (10)
- Dew point temperature (avg.)
- (11)
- P—phosphorus
- (12)
- Mn—manganese
- (13)
- NDVI 40 days before harvest (avg.)
- (14)
- Fe—iron
- (15)
- RDVI 40 days before harvest (avg.)
- (16)
- Fertigation (BBCH 0–60)
- (17)
- SAVI 40 days before harvest (avg.)
- (18)
- NDVI 40 days before harvest (max.)
- (19)
- pH
- (20)
- B—boron
- (21)
- C—calcium
- (22)
- NDVI 40 days before harvest (min.)
- (23)
- EVI 40 days before harvest (min.)
- (24)
- N-NO3—nitroge
- (25)
- Fertilization (BBCH 61–70)
- (26)
- EVI 40 days before harvest (max.)
- (27)
- Na—sodium
- (28)
- K—potassium
- (29)
- Soil P—phosphorus (avg.) (BBCH 61–70)
- (30)
- SAVI 40 days before harvest (max.)
- (31)
- HTC (BBCH 61–70)
- (32)
- Fertigation
- (33)
- RDVI 40 days before harvest (min.)
- (34)
- HTC (BBCH > 70)
- (35)
- Soil P—phosphorus (avg.) (BBCH > 70)
- (36)
- Irrigation (BBCH 0–60)
- (37)
- K-potassium-Fertilization (annually)
- (38)
- Cu—copper
- (39)
- S-SO—sulfur
- (40)
- Rainfall (BBCH 0–60)
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Subplot Code | Variety | No of Subplots | Total Area [ha] |
---|---|---|---|
102 | Nelson | 18 | 26.16 |
103 | Nelson | 12 | 26.4 |
104 | Nelson | 12 | 16.2 |
105 | Nelson | 18 | 26.22 |
106 | Nelson | 6 | 12.48 |
107 | Chandler | 18 | 32.34 |
108 | Chandler | 18 | 29.82 |
109 | Chandler | 12 | 15.9 |
110 | Chandler | 12 | 17.22 |
111 | Chandler | 6 | 13.98 |
112 | Chandler | 12 | 23.46 |
113 | Chandler | 12 | 22.2 |
114 | Liberty | 12 | 17.1 |
115 | Liberty | 18 | 26.82 |
116 | Liberty | 12 | 22.2 |
117 | Liberty | 12 | 23.22 |
118 | Liberty | 11 | 10.07 |
120 | Chandler | 5 | 8.65 |
121 | Chandler | 5 | 8.8 |
122 | Nelson | 6 | 5.82 |
123 | Nelson | 6 | 8.28 |
Total | 243 | 393.34 |
Features Group | No. of Raw Data |
---|---|
Treatment features | 135,113 |
Weather features | 831,562 |
Soil features | 6929 |
BBCH soil features | 7380 |
Vegetation features | 3936 |
Selyaninov hydrothermal coefficient | 738 |
GDD features | 738 |
Aggregates based on fertilization and fertigation | 9045 |
Harmful features | 110 |
Total | 995,551 |
Features Group | No. of Features |
---|---|
Treatment features | 2 |
Weather features | 10 |
Soil features | 14 |
BBCH soil features | 30 |
Vegetation features | 16 |
Sjeljaninow features | 3 |
GDD features | 3 |
Aggregates based on fertilization and fertigation | 9 |
Harmful features | 2 |
Total | 89 |
No. | Feature with Missing Values | % of Missing Values |
---|---|---|
1 | S-SO—sulfur | 17% |
2 | Cl—chlorine | 33% |
3 | Irrigation (BBCH 0–60) | 46% |
4 | Fertilization (BBCH 0–60) | 47% |
5 | Hailstorm percentage of damage | 67% |
6 | Hailstorm cut fruit | 87% |
No. | Classifier |
---|---|
1 | Linear regression |
2 | Ridge |
3 | Lasso |
4 | ElasticNet |
5 | XGB (learning_rate = 0.1, n_estimators = 1000, max_depth = 6) |
6 | Random Forest (max_depth = 3, n_estimators = 300) |
7 | MLP (hidden_layer_sizes = 10) |
8 | MLP (hidden_layer_sizes = 100) |
9 | SGD |
10 | NuSVR (nu = 0.2, C = 0.2,kernel = ’rbf’, gamma = 0.001) |
11 | SVR (C = 30,000.0, epsilon = 0.2) |
Classifier | No. of Cols | Step Wise | P-Enter | P-Remove | Pearson | Chi2 | PCA | PCA Comp. | MAPE Val. [%] | MAPE Test [%] |
---|---|---|---|---|---|---|---|---|---|---|
XGB | 40 | Yes | 0.33 | 0.38 | No | No | Yes | 8 | 10.33 | 12.48 |
Random | ||||||||||
Forest | 39 | Yes | 0.24 | 0.29 | No | No | Yes | 9 | 10.20 | 14.30 |
Linear | ||||||||||
Regression | 48 | Yes | 0.44 | 0.49 | No | No | Yes | 9 | 5.70 | 15.90 |
SVR | 37 | Yes | 0.20 | 0.25 | No | No | Yes | 6 | 15.09 | 15.96 |
Lasso | 48 | Yes | 0.44 | 0.49 | No | No | Yes | 8 | 1.49 | 17.68 |
SGD | 48 | Yes | 0.44 | 0.49 | No | No | Yes | 9 | 1.22 | 17.94 |
Ridge | 48 | Yes | 0.44 | 0.49 | No | No | Yes | 9 | 0.49 | 18.64 |
ElasticNet | 37 | Yes | 0.20 | 0.25 | No | No | Yes | 5 | 13.73 | 21.84 |
NuSVR | 37 | Yes | 0.20 | 0.25 | No | No | Yes | 4 | 31.81 | 34.91 |
MLP(100) | 89 | No | 0 | 0 | No | No | No | 0 | 97.48 | 98.25 |
MLP(10) | 46 | Yes | 0.43 | 0.48 | No | No | No | 0 | 99.70 | 99.76 |
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Share and Cite
Niedbała, G.; Kurek, J.; Świderski, B.; Wojciechowski, T.; Antoniuk, I.; Bobran, K. Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods. Agriculture 2022, 12, 2089. https://doi.org/10.3390/agriculture12122089
Niedbała G, Kurek J, Świderski B, Wojciechowski T, Antoniuk I, Bobran K. Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods. Agriculture. 2022; 12(12):2089. https://doi.org/10.3390/agriculture12122089
Chicago/Turabian StyleNiedbała, Gniewko, Jarosław Kurek, Bartosz Świderski, Tomasz Wojciechowski, Izabella Antoniuk, and Krzysztof Bobran. 2022. "Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods" Agriculture 12, no. 12: 2089. https://doi.org/10.3390/agriculture12122089