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A review of deep learning techniques used in agriculture
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 …
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
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 …
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
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 …
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
Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology,
and water resources engineering, and its accurate and cost-effective determination and …
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
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) …
due to poor weather conditions (eg, clouds, shadows, and other atmospheric conditions) …
A review of machine learning approaches to soil temperature estimation
Soil temperature is an essential factor for agricultural, meteorological, and hydrological
applications. Direct measurement, despite its high accuracy, is impractical on a large spatial …
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
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 …
A cost-effective model that can accurately forecast soil temperature is urgently needed …
Multi-depth daily soil temperature modeling: meteorological variables or time series?
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 …
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
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 …
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
Soil temperature is a critical factor in soil science, hydrology, agriculture, water resources
engineering, geotechnical engineering, geo-environmental engineering, meteorology, and …
engineering, geotechnical engineering, geo-environmental engineering, meteorology, and …