相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression
A'kif Al-Fugara et al.
GEOCARTO INTERNATIONAL (2022)
Evaluation of hydroponic systems for the cultivation of Lettuce (Lactuca sativa L., var. Longifolia) and comparison with protected soil-based cultivation
Maliqa Majid et al.
AGRICULTURAL WATER MANAGEMENT (2021)
Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models
Junliang Fan et al.
AGRICULTURAL WATER MANAGEMENT (2021)
Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt
Mohamed K. Abdel-Fattah et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021)
Iodine biofortification of sweet basil and lettuce grown in two hydroponic systems
M. Puccinelli et al.
SCIENTIA HORTICULTURAE (2021)
Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches
Juan Cao et al.
AGRICULTURAL AND FOREST METEOROLOGY (2021)
Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms
Ali Mokhtar et al.
IEEE ACCESS (2021)
Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods
Elisa Kamir et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)
The Impact of Advanced Static Magnetic Units on Water Properties and the Performance of Aeroponic and NFT Systems for Lettuce
Wessam El-Ssawy et al.
POLISH JOURNAL OF ENVIRONMENTAL STUDIES (2020)
From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management
Veronica Saiz-Rubio et al.
AGRONOMY-BASEL (2020)
A CNN-RNN Framework for Crop Yield Prediction
Saeed Khaki et al.
FRONTIERS IN PLANT SCIENCE (2020)
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning
Lucas Borges Ferreira et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
Machine learning applications in production lines: A systematic literature review
Ziqiu Kang et al.
COMPUTERS & INDUSTRIAL ENGINEERING (2020)
The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt
Ahmed Elbeltagi et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2020)
Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations
Aleksandra Wolanin et al.
REMOTE SENSING OF ENVIRONMENT (2019)
A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat
Syed Haleem Shah et al.
REMOTE SENSING (2019)
Crop Yield Prediction Using Deep Neural Networks
Saeed Khaki et al.
FRONTIERS IN PLANT SCIENCE (2019)
A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006-2015
Nan Kim et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Using Machine Learning and Multi-Element Analysis to Evaluate the Authenticity of Organic and Conventional Vegetables
Eloa Moura Araujo et al.
FOOD ANALYTICAL METHODS (2019)
The effects of phosphorus supply limitation on photosynthesis, biomass production, nutritional quality, and mineral nutrition in lettuce grown in a recirculating nutrient solution
Damianos Neocleous et al.
SCIENTIA HORTICULTURAE (2019)
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
Yaping Cai et al.
AGRICULTURAL AND FOREST METEOROLOGY (2019)
Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction
Sujan Ghimire et al.
ENERGIES (2019)
Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm
Saman Maroufpoor et al.
JOURNAL OF HYDROLOGY (2019)
Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
C. Folberth et al.
AGRICULTURAL AND FOREST METEOROLOGY (2019)
Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models
Kevin O. Achieng
COMPUTERS & GEOSCIENCES (2019)
Equipment and Intelligent Control System in Aquaponics: A Review
Yaoguang Wei et al.
IEEE ACCESS (2019)
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
Junliang Fan et al.
ENERGY CONVERSION AND MANAGEMENT (2018)
Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq
Sarchil Hama Qader et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2018)
IoT based hydroponics system using Deep Neural Networks
Manav Mehra et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)
Design of Aquaponics Water Monitoring System Using Arduino Microcontroller
S. A. Z. Murad et al.
3RD ELECTRONIC AND GREEN MATERIALS INTERNATIONAL CONFERENCE 2017 (EGM 2017) (2017)
Accurate prediction of sugarcane yield using a random forest algorithm
Yvette Everingham et al.
AGRONOMY FOR SUSTAINABLE DEVELOPMENT (2016)
Predicting bubble size and bubble rate data in water and in froth flotation-like slurry from computational fluid dynamics (CFD) by applying deep neural networks (DNN)
Gonzalo Montes-Atenas et al.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER (2016)
Random Forests for Global and Regional Crop Yield Predictions
Jig Han Jeong et al.
PLOS ONE (2016)
Comparison of solar radiation models and their validation under Algerian climate - The case of direct irradiance
Omar Behar et al.
ENERGY CONVERSION AND MANAGEMENT (2015)
A new concept for aquaponic systems to improve sustainability, increase productivity, and reduce environmental impacts
Werner Kloas et al.
AQUACULTURE ENVIRONMENT INTERACTIONS (2015)
An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
David M. Johnson
REMOTE SENSING OF ENVIRONMENT (2014)
A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects
Christian A. Gueymard
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2014)
Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes
Shinji Fukuda et al.
AGRICULTURAL WATER MANAGEMENT (2013)
General models for estimating daily global solar radiation for different solar radiation zones in mainland China
Mao-Fen Li et al.
ENERGY CONVERSION AND MANAGEMENT (2013)
Direct monitoring of the electrostatic charge of house-flies (Musca domestica L.) as they walk on a dielectric surface
C Jackson et al.
JOURNAL OF ELECTROSTATICS (2005)
分享论文
