A review of deep learning techniques used in agriculture

I Attri, LK Awasthi, TP Sharma, P Rathee - Ecological Informatics, 2023 - Elsevier
Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …

A review of the challenges of using deep learning algorithms to support decision-making in agricultural activities

K Alibabaei, PD Gaspar, TM Lima, RM Campos… - Remote Sensing, 2022 - mdpi.com
Deep Learning has been successfully applied to image recognition, speech recognition, and
natural language processing in recent years. Therefore, there has been an incentive to …

Spatio-temporal forecasting: A survey of data-driven models using exogenous data

S Berkani, B Guermah, M Zakroum, M Ghogho - IEEE Access, 2023 - ieeexplore.ieee.org
Forecasting Spatio-Temporal processes has been attracting a great deal of interest within
the research community. In this context, there is an increasing trend of proposing and …

A convolutional neural network model for soil temperature prediction under ordinary and hot weather conditions: comparison with a multilayer perceptron model

V Farhangmehr, JH Cobo, A Mohammadian, P Payeur… - Sustainability, 2023 - mdpi.com
Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology,
and water resources engineering, and its accurate and cost-effective determination and …

Combining ATC and 3D-CNN for reconstructing spatially and temporally continuous land surface temperature

H Fu, Z Shao, P Fu, X Huang, T Cheng, Y Fan - International Journal of …, 2022 - Elsevier
More than half of the satellite-derived Land surface temperatures (LSTs) data are missing
due to poor weather conditions (eg, clouds, shadows, and other atmospheric conditions) …

A review of machine learning approaches to soil temperature estimation

M Taheri, HK Schreiner, A Mohammadian, H Shirkhani… - Sustainability, 2023 - mdpi.com
Soil temperature is an essential factor for agricultural, meteorological, and hydrological
applications. Direct measurement, despite its high accuracy, is impractical on a large spatial …

[HTML][HTML] A comprehensive study of artificial intelligence applications for soil temperature prediction in ordinary climate conditions and extremely hot events

H Imanian, J Hiedra Cobo, P Payeur, H Shirkhani… - Sustainability, 2022 - mdpi.com
Soil temperature is a fundamental parameter in water resources and irrigation engineering.
A cost-effective model that can accurately forecast soil temperature is urgently needed …

Multi-depth daily soil temperature modeling: meteorological variables or time series?

I Ebtehaj, H Bonakdari, P Samui… - Theoretical and Applied …, 2023 - Springer
This study presents the first-time application of a novel emotional neural network (ENN) for
soil temperature modeling. Two scenarios were considered for soil temperature …

Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards

K Alibabaei, PD Gaspar, RM Campos, GC Rodrigues… - Applied Sciences, 2023 - mdpi.com
As agriculture has an increasing impact on the environment, new techniques can help meet
future food needs while maintaining or reducing the environmental footprint. Those …

A spatiotemporal CNN-LSTM deep learning model for predicting soil temperature in diverse large-scale regional climates

V Farhangmehr, H Imanian, A Mohammadian… - Science of The Total …, 2025 - Elsevier
Soil temperature is a critical factor in soil science, hydrology, agriculture, water resources
engineering, geotechnical engineering, geo-environmental engineering, meteorology, and …