Evolutionary feature selection for yield prediction of subtropical crops

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Abstract Accurate crop yield forecasting requires identifying informative predictors from high-dimensional meteorological time series with multiple time lags. Such settings often lead to overfitting, reduced interpretability, and increased computational cost. This paper proposes a two-phase feature-selection framework, termed CS $$_\text {MRMR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mtext>MRMR</mml:mtext> <mml:mrow/> </mml:mmultiscripts> </mml:math> -DWES $$_R$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mi>R</mml:mi> <mml:mrow/> </mml:mmultiscripts> </mml:math> , to address these challenges in agricultural time-series regression. In the first phase, a supervised filter ranks features using correlation statistics with a maximum relevance-minimum redundancy criterion, retaining a compact subset of informative variables. In the second phase, a wrapper-based discrete weighted evolutionary strategy automatically selects the optimal feature subset by directly optimizing the predictive performance of a SARIMAX regression model. The proposed framework is evaluated on 20 years of monthly avocado and mango yield data from the Axarquía region of Málaga in Spain under multiple lag configurations. This feature selection might slightly change over time and depends entirely on the crop type and geographical location. The proposed CS $$_\text {MRMR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mtext>MRMR</mml:mtext> <mml:mrow/> </mml:mmultiscripts> </mml:math> -DWES $$_\text {R}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mtext>R</mml:mtext> <mml:mrow/> </mml:mmultiscripts> </mml:math> framework inherently requires high-performance computing (HPC) capabilities due to the scale and complexity of the agricultural time-series datasets under study. Experimental results show that CS $$_\text {MRMR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mtext>MRMR</mml:mtext> <mml:mrow/> </mml:mmultiscripts> </mml:math> -DWES $$_R$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mi>R</mml:mi> <mml:mrow/> </mml:mmultiscripts> </mml:math> improves forecasting accuracy while reducing feature dimensionality and computational cost compared with baseline and existing feature-selection methods. These findings highlight the effectiveness of the proposed approach for high-dimensional agricultural time-series analysis.

Links and sources

• PDF from original source

Comments