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Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM)

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EUROPEAN JOURNAL OF AGRONOMY 2025年 167卷

作者： Dastres, Emran Esmaeili, Hassan Edalat, Mohsen Shahid Beheshti Univ Med Plants & Drugs Res Inst Dept Agr Tehran *** Iran Shiraz Univ Sch Agr Plant Prod & Genet Dept Shiraz Iran

The integration of artificial intelligence (AI) and machine learning algorithms (MLAs) has transformed ecological modeling and precision agriculture by improving the prediction of plant species distribution. This advancement is particularly significant in agricultural practices like weed management, where precision and sustainability are critical for enhancing farming efficiency and environmental conservation. The study conducted in Fars province, Iran was aimed to apply species distribution modeling (SDM) to site-specific weed management (SSWM) in rapeseed (Brassica napus L.) fields. The focus was on improving precision in control of the weed Malva neglecta, using advanced machine learning algorithms. The study employed machine learning algorithms, including Random Forest (RF), Boosted Regression Trees (BRT), Support Vector Machine (SVM), Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), k-Nearest Neighbors (KNN), Generalized Additive Model (GAM), Generalized Boosting Model (GBM), Classification and Regression Trees (CART), and Multivariate Adaptive Regression Splines (MARS) to predict the distribution of M. neglecta. Geographic Information Systems (GIS) were used to create raster maps from eleven environmental factors for spatial analysis. A multi-collinearity test using the variance inflation factor (VIF) ensured model robustness, and factor importance was analyzed with Boruta, RF, Generalized Linear Model (GLM), and Partial Least Squares (PLS) algorithms. The study found that machine learning algorithms, particularly RF, CART, MARS, and BRT, provided highly accurate predictions of M. neglecta distribution. The analysis identified mean annual rainfall, slope, and silt percentage as key environmental factors influencing the distribution of M. neglecta. The successful application of these algorithms in predicting weed distribution demonstrates their potential for improving ecological modeling. The study highlights the effectiveness of SDM i

关键词： data usage technique Environmental factors Spatial modeling Machine learning algorithms Malva neglecta Weed distribution

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