A comparison of numerical and machine-learning modeling of soil water content with limited input data

F Karandish, J Šimůnek - Journal of Hydrology, 2016 - Elsevier
Soil water content (SWC) is a key factor in optimizing the usage of water resources in
agriculture since it provides information to make an accurate estimation of crop water …

Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach

LS Pereira, P Paredes, N Jovanovic - Agricultural water management, 2020 - Elsevier
This study reviews soil water balance (SWB) model approaches to determine crop irrigation
requirements and scheduling irrigation adopting the FAO56 method. The K c-ET o approach …

Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics

P Li, Y Zha, L Shi, CHM Tso, Y Zhang, W Zeng - Journal of Hydrology, 2020 - Elsevier
Soil moisture plays a critical role as an essential component of the global water resources by
regulating mass and energy exchange between land surface and atmosphere …

Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water …

A Katimbo, DR Rudnick, J Zhang, Y Ge… - Smart Agricultural …, 2023 - Elsevier
Irrigation water management using automated irrigation decision support system (IDSS) as a
smart irrigation scheduling tool can improve water use efficiency and crop production …

Prediction of irrigation water requirements for green beans-based machine learning algorithm models in arid region

A Mokhtar, N Al-Ansari, W El-Ssawy, R Graf… - Water resources …, 2023 - Springer
Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited
available meteorological data to calculate reference evapotranspiration. Consequently, the …

Crop yield simulation optimization using precision irrigation and subsurface water retention technology

PC Roy, A Guber, M Abouali, AP Nejadhashemi… - … Modelling & Software, 2019 - Elsevier
Maximizing crop production with minimal resources such as water and energy is the primary
focus of sustainable agriculture. Subsurface water retention technology (SWRT) is a stable …

Soil moisture forecast for smart irrigation: The primetime for machine learning

R Togneri, DF dos Santos, G Camponogara… - Expert Systems with …, 2022 - Elsevier
The rise of the Internet of Things allowed higher spatial–temporal resolution soil moisture
data captured through in situ sensing. Such abundance of data enables machine learning …

Machine learning modeling of water footprint in crop production distinguishing water supply and irrigation method scenarios

Z Li, W Wang, X Ji, P Wu, L Zhuo - Journal of Hydrology, 2023 - Elsevier
Crop water footprint (WF) calculation via physical process-based crop or hydrological
models has basic requirements in terms of cost, time, and knowledge threshold. It is …

Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms

M Jamei, M Karbasi, A Malik, M Jamei, O Kisi… - Agricultural Water …, 2022 - Elsevier
The root zone soil moisture (RZSM) is essential for monitoring and forecasting agricultural,
hydrological, and meteorological systems. Accordingly, researchers are determined to …

A deep learning approach for multi-depth soil water content prediction in summer maize growth period

J Yu, S Tang, L Zhangzhong, W Zheng, L Wang… - IEEE …, 2020 - ieeexplore.ieee.org
Advance knowledge of soil water content (SWC) in the soil wetting layer of crop irrigation
can help develop more reasonable irrigation plans and improve the efficiency of agricultural …