Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations
Abstract
:1. Introduction
2. Materials and Methods
- (1)
- An exploratory analysis of four input crop spraying coverage factors according to four independent technical factors of spraying: nozzle type, working speed, spraying norm, and crop type;
- (2)
- An ensemble machine learning prediction of these leaf surface spraying coverage factors and an accuracy assessment based on 10-fold cross-validation in 10 repetitions per model;
- (3)
- A relative variable importance assessment according to the independent technical factors of crop spraying used for prediction.
2.1. Field Research and Adjustment of Technical Factors of Spraying
2.2. Machine Learning Methods for Prediction and Accuracy Assessment
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Independent Variable | Method | Optimal Hyperparameters | |
---|---|---|---|
Axial | Radial | ||
Area coverage (%) | QRF | mtry = 2 | mtry = 2 |
SVR | sigma = 0.162, C = 2 | sigma = 0.162, C = 2 | |
BRNN | neurons = 3 | neurons = 3 | |
Droplets per cm2 (n) | QRF | mtry = 2 | mtry = 3 |
SVR | sigma = 0.159, C = 64 | sigma = 0.198, C = 64 | |
BRNN | neurons = 8 | neurons = 8 | |
Droplet diameter (μm) | QRF | mtry = 2 | mtry = 2 |
SVR | sigma = 0.379, C = 0.5 | sigma = 0.368, C = 4 | |
BRNN | neurons = 10 | neurons = 7 | |
Drift (%) | QRF | mtry = 4 | mtry = 4 |
SVR | sigma = 0.162, C = 128 | sigma = 0.378, C = 64 | |
BRNN | neurons = 9 | neurons = 10 |
Independent Variable | Method | Axial | Radial | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | ||
Area coverage (%) | QRF | 0.694 | 4.90 | 0.128 | 0.770 | 5.79 | 0.119 |
SVR | 0.737 | 4.60 | 0.120 | 0.781 | 5.73 | 0.118 | |
BRNN | 0.745 | 4.55 | 0.119 | 0.761 | 5.99 | 0.123 | |
ENS | 0.732 | 4.54 | 0.118 | 0.754 | 5.91 | 0.122 | |
Droplets per cm2 (n) | QRF | 0.916 | 7.86 | 0.095 | 0.894 | 9.61 | 0.111 |
SVR | 0.900 | 8.04 | 0.097 | 0.891 | 9.68 | 0.112 | |
BRNN | 0.921 | 7.32 | 0.088 | 0.893 | 9.56 | 0.111 | |
ENS | 0.916 | 7.65 | 0.092 | 0.891 | 9.57 | 0.111 | |
Droplet diameter (μm) | QRF | 0.437 | 23.33 | 0.129 | 0.700 | 17.02 | 0.099 |
SVR | 0.454 | 22.85 | 0.126 | 0.691 | 17.00 | 0.099 | |
BRNN | 0.503 | 21.28 | 0.118 | 0.719 | 16.54 | 0.096 | |
ENS | 0.499 | 21.15 | 0.117 | 0.750 | 16.16 | 0.094 | |
Drift (%) | QRF | 0.981 | 0.70 | 0.037 | 0.971 | 0.84 | 0.050 |
SVR | 0.977 | 0.78 | 0.041 | 0.974 | 0.83 | 0.049 | |
BRNN | 0.983 | 0.70 | 0.037 | 0.975 | 0.80 | 0.047 | |
ENS | 0.984 | 0.72 | 0.038 | 0.979 | 0.80 | 0.047 |
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Tadić, V.; Radočaj, D.; Jurišić, M. Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations. Agronomy 2024, 14, 1977. https://doi.org/10.3390/agronomy14091977
Tadić V, Radočaj D, Jurišić M. Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations. Agronomy. 2024; 14(9):1977. https://doi.org/10.3390/agronomy14091977
Chicago/Turabian StyleTadić, Vjekoslav, Dorijan Radočaj, and Mladen Jurišić. 2024. "Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations" Agronomy 14, no. 9: 1977. https://doi.org/10.3390/agronomy14091977