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Table 13 Machine learning for crop yield forecasting

From: Soybean cyst nematode detection and management: a review

References

Crop type

ML algorithm

Features

Evaluation parameter

[131]

Soybean (Glycine Max)

Deep CNN-LSTM

MODIS (LS, and SR) Weather datam

Avg RMSE

[128]

Soybean (Glycine Max)

SVM, RF, and MLP

Spectral reflectance bands

RF (84%)

[132]

Soybean (Glycine Max)

LSTM, LR, Random forest

NDVI, EVI, Land surface temperature

Mean absolute error

[139]

Soybean yields and corn

Regression trees

NDVI, precipitation, LST

\(R^2\), RMSE

[133]

Soybean yields and corn

Scalable ML (DNN-LSTM)

NDVI, Precipitation, LST

\(R^2\), RMSE, MAE, and MAPE

[140]

Soybean (Glycine Max)

PLSR, SVR, DNN-F1, DNN-F2

Canopy spectral, structure, thermal and texture features

\(R^2\) of 0.720 and a (RMSE%) of 15.9%