From: Predicting rice blast disease: machine learning versus process-based models
Model | Early warning Success – AUC metric | ||||
---|---|---|---|---|---|
Kalochori 2016 | Seville 2016 | Kalochori 2015 | Portugal 2015 | Average of Normalized valuesb | |
Yoshino | 28 | 46 | 38 | N/Aa | 0.74 |
WARM | 38 | N/Aa | 18 | 12 | 0.77 |
M5Rules | 38 | 88 | 15 | 12 | 0.80 |
LSTM NN | 39 | 62 | 13 | 14 | 0.76 |