Integration of UAV images and ensemble learning for root zone soil moisture estimation in sorghum
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate estimation of root-zone soil moisture (SM) is critical for agricultural water management and sustainable crop production. This study develops and evaluates a methodology to estimate SM in sorghum root zones by high-resolution unmanned aerial vehicle (UAV) multispectral and thermal imagery with machine learning (ML). A two-season field experiment (2020-2021) with four irrigation regimes provided UAV data and concurrent ground-based SM measurements. This study conducted a comparative analysis of four ML algorithms: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGB), and K-Nearest Neighbors (KNN). The models were evaluated both as standalone predictors and as components of ensemble structures. Among single models, RF achieved the highest test performance (R-2 = 0.84, RMSE = 11.22 mm/90 cm, MAE = 9.32 mm/90 cm). An ensemble combining XGBoost, LGB, and KNN yielded a slight improvement (R-2 = 0.85, RMSE = 11.124 mm/90 cm, MAE = 8.775 mm/90 cm), indicating that ensemble learning can modestly enhance model performance. The proposed workflow offers a practical approach for field-scale SM monitoring, demonstrating potential applications in irrigation scheduling and agricultural water management.
Açıklama
Anahtar Kelimeler
[Keyword Not Available]
Kaynak
Irrigation Science
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
44
Sayı
1
