A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials

W Peng, O Karimi Sadaghiani - Energy Sources, Part A: Recovery …, 2023‏ - Taylor & Francis
The application of biomass, as an energy resource, depends on four main steps of
production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning …

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

J Lu, H Fu, X Tang, Z Liu, J Huang, W Zou, H Chen… - Scientific Reports, 2024‏ - nature.com
Accurately estimating large-area crop yields, especially for soybeans, is essential for
addressing global food security challenges. This study introduces a deep learning …

A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data

X Cui, Z Wang, N Xu, J Wu, Z Yao - Environmental Modelling & Software, 2024‏ - Elsevier
Groundwater level (GWL) prediction is important for ecological protection and resource
utilization; it helps in formulating policies for artificial groundwater recharge, modifying the …

Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms

W Peng, OK Sadaghiani - Biomass and Bioenergy, 2023‏ - Elsevier
Forest is considered a significant source of woody biomass production. Sustainable
production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and …

GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania

S Jeong, Y Ryu, X Li, B Dechant, J Liu, J Kong… - Remote Sensing of …, 2024‏ - Elsevier
The diurnal solar-induced chlorophyll fluorescence (SIF) sampling capability of OCO-3 can
provide crucial insights into ecosystem function at the sub-daily scale. However, potential …

Deep learning for multi-source data-driven crop yield prediction in northeast China

J Lu, J Li, H Fu, X Tang, Z Liu, H Chen, Y Sun, X Ning - Agriculture, 2024‏ - mdpi.com
The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and
ensuring food security. This study assesses the performance of the CNN-LSTM-Attention …

The potential of NIRvP in estimating evapotranspiration

C Ersi, B Sudu, Z Song, Y Bao, S Wei, J Zhang… - Remote Sensing of …, 2024‏ - Elsevier
Accurate estimation of regional-scale evapotranspiration (ET) and reference
evapotranspiration (ET o) is crucial for scientific and rational water resource management …

Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF)

X Kang, C Huang, L Zhang, H Wang, Z Zhang… - Remote Sensing of …, 2023‏ - Elsevier
Solar-induced chlorophyll fluorescence (SIF), as a direct probe of vegetation photosynthesis,
has recently been an effective indicator for crop yield estimation in late-season. Spatio …

[HTML][HTML] A multi-layer perceptron approach for SIF retrieval in the O2-A absorption band from hyperspectral imagery of the HyPlant airborne sensor system

J Buffat, M Pato, K Alonso, S Auer, E Carmona… - Remote Sensing of …, 2025‏ - Elsevier
Accurate estimation of solar-induced fluorescence (SIF) from passively sensed
hyperspectral remote sensing data has been identified as fundamental in assessing the …

A leaf age‐dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests

J Tian, X Yang, W Yuan, S Lin, L Han… - Global Change …, 2024‏ - Wiley Online Library
Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one‐
third of terrestrial gross primary productivity (GPP). However, the continental‐scale leaf …