random forest Concept

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research area of

• Gumma, Muralikrishna, Head Remote Sensing - GIS Lab, ICRISAT, ICRISAT
• Jimenez, Daniel, Scientist, The Alliance of Bioversity International and CIAT, The Alliance of Bioversity and CIAT
• Laborte, Alice G., Scientist, IRRI, IRRI
• Selvaraj, Michael, Scientist, The Alliance of Bioversity International and CIAT, The Alliance of Bioversity and CIAT

subject area of

• A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform
• A Proposal to Estimate the Variable Importance Measures in Predictive Models Using Results from a Wrapper
• Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
• Alternative approaches to assessing the natural regeneration of Scots pine in a Mediterranean forest
• Assessment of Machine Learning Algorithms for Prediction of Breast Cancer Malignancy Based on Mammogram Numeric Data
• Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods
• Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices
• Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest
• Consistent forest change maps 1981–2000 from the AVHRR time series: Case studies for South America and Indonesia
• Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)
• Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
• Detecting industrial oil palm plantations on Landsat images with Google Earth Engine
• Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin
• Digital Disease Phenotyping
• Effects of image pansharpening on soil total nitrogen prediction models in South India
• Enhancing the performance of regional land cover mapping
• Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.).
• Finding Optimal Farming Practices to Increase Crop Yield Through Global-Best Harmony Search and Predictive Models, a Data-Driven Approach.
• Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam
• Genomic-enabled prediction with classification algorithms.
• Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
• Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series
• MAIZE YIELD ESTIMATION IN KENYA USING MODIS
• MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE
• Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).
• Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud
• Methods for estimating population density in data-limited areas: evaluating regression and tree-based models in Peru.
• Opportunities for expanding paddy rice production in Laos: spatial predictive modeling using Random Forest
• Predicting alpha diversity of African rain forests: models based on climate and satellite‐derived data do not perform better than a purely spatial model
• Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE
• Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)
• Random forest regression for optimizing variable planting rates for corn and soybean using topographical and soil data
• Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq
• Untangling crop management and environmental influences on wheat yield variability in Bangladesh: An application of non-parametric approaches
• Using Random Forest to Improve the Downscaling of Global Livestock Census Data.