Optimization and Validation of Two Machine Learning Algorithms for Accurate Prediction of Irrigated Wheat (Triticum aestivum L.) Yield and Identification of its Influential Factors in Khorasan Razavi Province

IntroductionThis study undertook a detailed comparison of two supervised machine-learning algorithms—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—to predict irrigated wheat (Triticum aestivum L.) yield across 20 counties in Razavi Khorasan Province. Both models were trained on 70 % of...

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Bibliografiske detaljer

Hovedforfatter:	M Jahan
Format:	Artigo
Sprog:	Persa
Udgivet:	Ferdowsi University of Mashhad 2025-12-01
Serier:	پژوهشهای زراعی ایران
Fag:	cross-validation grid search random forest xgboost yield class
Online adgang:	https://jcesc.um.ac.ir/article_47053_29bed63ac826b7cf6c0f8097e9448396.pdf
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