4.7 Article

Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

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。
Review Agriculture, Multidisciplinary

Crop yield prediction using machine learning: A systematic literature review

Thomas van Klompenburg et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Article Remote Sensing

Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging

Yuri Shendryk et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2020)

Proceedings Paper Computer Science, Artificial Intelligence

A SATELLITE-BASED METHODOLOGY FOR HARVEST DATE DETECTION AND YIELD PREDICTION IN SUGARCANE

Yuri Shendryk et al.

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (2020)

Article Engineering, Electrical & Electronic

Leveraging High-Resolution Satellite Imagery and Gradient Boosting for Invasive Weed Mapping

Yuri Shendryk et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

Article Computer Science, Artificial Intelligence

From local explanations to global understanding with explainable AI for trees

Scott M. Lundberg et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Environmental Sciences

Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery

Ramses A. Molijn et al.

REMOTE SENSING (2019)

Review Agriculture, Multidisciplinary

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

Anna Chlingaryan et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Remote Sensing

Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble

Jeferson Lobato Fernandes et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

Article Agronomy

Accurate prediction of sugarcane yield using a random forest algorithm

Yvette Everingham et al.

AGRONOMY FOR SUSTAINABLE DEVELOPMENT (2016)

Article Agriculture, Multidisciplinary

When do I want to know and why? Different demands on sugarcane yield predictions

Felipe Ferreira Bocca et al.

AGRICULTURAL SYSTEMS (2015)

Article Soil Science

Soil and Landscape Grid of Australia

M. J. Grundy et al.

SOIL RESEARCH (2015)

Article Remote Sensing

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

A. Begue et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2010)

Article Agriculture, Multidisciplinary

Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery

Isabel Luisa Castillejo-Gonzalez et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2009)

Article Computer Science, Artificial Intelligence

Input data for decision trees

Selwyn Piramuthu

EXPERT SYSTEMS WITH APPLICATIONS (2008)

Article Agriculture, Multidisciplinary

A Bayesian modelling approach for long lead sugarcane yield forecasts for the Australian sugar industry

Y. L. Everingham et al.

AUSTRALIAN JOURNAL OF AGRICULTURAL RESEARCH (2007)

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)

Article Geochemistry & Geophysics

Multiple classifiers applied to multisource remote sensing data

GJ Briem et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2002)

Article Agriculture, Multidisciplinary

Factors affecting cane yield and commercial cane sugar in the Tully district

RA Lawes et al.

AUSTRALIAN JOURNAL OF EXPERIMENTAL AGRICULTURE (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)