Código QR (código de barras bidimensional)

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...

תיאור מלא

שמור ב:
מחבר ראשי: M Jahan
פורמט: Artigo
שפה:Persa
יצא לאור: Ferdowsi University of Mashhad 2025-12-01
סדרה:پژوهشهای زراعی ایران
נושאים:
cross-validation
grid search
random forest
xgboost
yield class
גישה מקוונת:https://jcesc.um.ac.ir/article_47053_29bed63ac826b7cf6c0f8097e9448396.pdf
תגים: הוספת תג
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!

פריטים דומים