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Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe
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DOI：10.3390/agronomy13051297.png)
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Abstract
En 中文
Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature degrees C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-(SC1) and ANN-MLP-(SC4) outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-(SC1)) and 110.7 (ANN-MLP-(SC4)). In the testing phase, the ANN-MLP-(SC4) had the highest r value (0.96), followed by ANN-MLP-(SC1) (0.94) and RF-(SC2) (0.94). The 10-fold cross validation also revealed that the ANN-MLP-(SC4) and ANN-MLP-(SC1) have the highest performance. We further evaluated the performance of the ANN-MLP-(SC4) in predicting maize yield on a regional scale (Budapest). The ANN-MLP-(SC4) succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.
Keywords:
maize yield
climate
multilayer perceptron
random forest
optimum model
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