Multistep ahead prediction of temperature and humidity in solar greenhouse based on FAM-LSTM model

Y Yang, P Gao, Z Sun, H Wang, M Lu, Y Liu… - … and Electronics in …, 2023 - Elsevier
Solar greenhouses offer favorable climatic environments for the production of off-season
crops in northern China. Greenhouse temperature and humidity are critical environmental …

A method of soil moisture content estimation at various soil organic matter conditions based on soil reflectance

T Li, T Mu, G Liu, X Yang, G Zhu, C Shang - Remote Sensing, 2022 - mdpi.com
Soil moisture is one of the most important components of all the soil properties affecting the
global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil …

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

H Rahman, UM Shah, SM Riaz, K Kifayat… - Future Generation …, 2024 - Elsevier
Due to the increase in world population, arable land has been reduced. Consequently, the
concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical …

Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds

M Zhang, Y Zhang, S Yu, Y Gao, J Dong, W Zhu… - Ecotoxicology and …, 2023 - Elsevier
Cyanobacteria blooms in aquaculture ponds harm the harvesting of aquatic animals and
threaten human health. Therefore, it is crucial to identify key drivers and develop methods to …

[BUCH][B] Patterns identification and data mining in weather and climate

A Hannachi - 2021 - Springer
Weather and climate is a fascinating system, which affects our daily lives, and is closely
interlinked with the environment, society and infrastructure. They have large impact on our …

Forecasting greenhouse air and soil temperatures: A multi-step time series approach employing attention-based LSTM network

X Li, L Zhang, X Wang, B Liang - Computers and Electronics in Agriculture, 2024 - Elsevier
Greenhouses stand as key infrastructural components in contemporary agriculture,
facilitating the perennial availability of vegetables. Harnessing accurate, real-time …

Knowledge transfer for adapting pre-trained deep neural models to predict different greenhouse environments based on a low quantity of data

T Moon, JE Son - Computers and Electronics in Agriculture, 2021 - Elsevier
Deep learning is the state-of-the-art application of machine learning in many fields, and this
technology has also been applied in agriculture. A large quantity of data needs to be …

Simulating canopy temperature using a random forest model to calculate the crop water stress index of chinese brassica

M Yang, P Gao, P Zhou, J **e, D Sun, X Han, W Wang - Agronomy, 2021 - mdpi.com
The determination of crop water status has positive effects on the Chinese Brassica industry
and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas …

Prediction of internal temperature in greenhouses using the supervised learning techniques: Linear and support vector regressions

F García-Vázquez, JR Ponce-González… - Applied Sciences, 2023 - mdpi.com
Agricultural greenhouses must accurately predict environmental factors to ensure optimal
crop growth and energy management efficiency. However, the existing predictors have …

A multi-model deep learning approach to address prediction imbalances in smart greenhouses

J Morales-García, F Terroso-Sáenz… - Computers and Electronics …, 2024 - Elsevier
The creation of smart greenhouses is playing a crucial role in paving the way toward
precision agriculture characterized by enhanced efficiency. Integral to these greenhouses …