Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield

The Impact of Advanced Static Magnetic Units on Water Properties and the Performance of Aeroponic and NFT Systems for Lettuce

(2020) Wessam El-Ssawy et al.   POLISH JOURNAL OF ENVIRONMENTAL STUDIES

Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

(2020) A'kif Al-Fugara et al.   Geocarto International

A CNN-RNN Framework for Crop Yield Prediction

(2020) Saeed Khaki et al.   Frontiers in Plant Science

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

(2020) Verónica Saiz-Rubio et al.   Agronomy-Basel

Evaluation of hydroponic systems for the cultivation of Lettuce (Lactuca sativa L., var. Longifolia) and comparison with protected soil-based cultivation

(2020) Maliqa Majid et al.   AGRICULTURAL WATER MANAGEMENT

Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models

(2020) Junliang Fan et al.   AGRICULTURAL WATER MANAGEMENT

Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

(2020) Mohamed K. Abdel-Fattah et al.   ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

Iodine biofortification of sweet basil and lettuce grown in two hydroponic systems

(2020) M. Puccinelli et al.   SCIENTIA HORTICULTURAE

Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches

(2020) Juan Cao et al.   AGRICULTURAL AND FOREST METEOROLOGY

Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations

(2019) Aleksandra Wolanin et al.   REMOTE SENSING OF ENVIRONMENT

A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

(2019) Syed Haleem Shah et al.   Remote Sensing

Crop Yield Prediction Using Deep Neural Networks

(2019) Saeed Khaki et al.   Frontiers in Plant Science

A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015

(2019) Nari Kim et al.   ISPRS International Journal of Geo-Information

Using Machine Learning and Multi-Element Analysis to Evaluate the Authenticity of Organic and Conventional Vegetables

(2019) Eloá Moura Araújo et al.   Food Analytical Methods

The effects of phosphorus supply limitation on photosynthesis, biomass production, nutritional quality, and mineral nutrition in lettuce grown in a recirculating nutrient solution

(2019) Damianos Neocleous et al.   SCIENTIA HORTICULTURAE

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

(2019) Yaping Cai et al.   AGRICULTURAL AND FOREST METEOROLOGY

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

(2019) Ghimire et al.   Energies

Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm

(2019) Saman Maroufpoor et al.   JOURNAL OF HYDROLOGY

Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models

(2019) Kevin O. Achieng   COMPUTERS & GEOSCIENCES

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods

(2019) Elisa Kamir et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

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

(2018) Junliang Fan et al.   ENERGY CONVERSION AND MANAGEMENT

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

(2018) Sarchil Hama Qader et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning

(2018) C. Folberth et al.   AGRICULTURAL AND FOREST METEOROLOGY

IoT based hydroponics system using Deep Neural Networks

(2018) Manav Mehra et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Accurate prediction of sugarcane yield using a random forest algorithm

(2016) Yvette Everingham et al.   Agronomy for Sustainable Development

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)

(2016) Gonzalo Montes-Atenas et al.   INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER

Random Forests for Global and Regional Crop Yield Predictions

(2016) Jig Han Jeong et al.   PLoS One

Comparison of solar radiation models and their validation under Algerian climate – The case of direct irradiance

(2015) Omar Behar et al.   ENERGY CONVERSION AND MANAGEMENT

Deep learning

(2015) Yann LeCun et al.   NATURE

A new concept for aquaponic systems to improve sustainability, increase productivity, and reduce environmental impacts

(2015) W Kloas et al.   Aquaculture Environment Interactions

A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects

(2014) Christian A. Gueymard   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

General models for estimating daily global solar radiation for different solar radiation zones in mainland China

(2013) Mao-Fen Li et al.   ENERGY CONVERSION AND MANAGEMENT

An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States

(2013) David M. Johnson   REMOTE SENSING OF ENVIRONMENT

Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes

(2012) Shinji Fukuda et al.   AGRICULTURAL WATER MANAGEMENT