Accurate prediction of sugarcane yield using a random forest algorithm

Management of wireworm damage in maize fields using new, landscape-scale strategies

(2015) Stéphanie Saussure et al.   Agronomy for Sustainable Development

Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach

(2015) Peng-Tao Guo et al.   GEODERMA

A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

(2015) J. García-Gutiérrez et al.   NEUROCOMPUTING

A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data

(2014) Piotr S. Gromski et al.   ANALYTICA CHIMICA ACTA

Prediction of N2O emission from local information with Random Forest

(2013) Aurore Philibert et al.   ENVIRONMENTAL POLLUTION

Sensing as a service model for smart cities supported by Internet of Things

(2013) Charith Perera et al.   Transactions on Emerging Telecommunications Technologies

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

Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

(2012) Elfatih M. Abdel-Rahman et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

(2012) Junfei Chen et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Climatic and genetic controls of yields of switchgrass, a model bioenergy species

(2011) Mirela G. Tulbure et al.   AGRICULTURE ECOSYSTEMS & ENVIRONMENT

Sucrose accumulation in sugarcane is influenced by temperature and genotype through the carbon source–sink balance

(2010) N. G. Inman-Bamber et al.   Crop & Pasture Science

Ensemble data mining approaches to forecast regional sugarcane crop production

(2008) Y.L. Everingham et al.   AGRICULTURAL AND FOREST METEOROLOGY

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS