Machine learning for large-scale crop yield forecasting (2020-12-14T00:00:00.000000Z)

TL;DR

This work combined agronomic principles of crop modeling with machine learning to build a machine learning baseline for large-scale crop yield forecasting and created features using crop simulation outputs and weather, remote sensing and soil data from the MCYFS database.

Authors

Dilli Paudel

1 papers

H. Boogaard

1 papers

A. D. Wit

1 papers

S. Janssen

1 papers

S. Osinga

1 papers

Christos Pylianidis

1 papers

I. Athanasiadis

1 papers

References77 items

Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Maize yield and nitrate loss prediction with machine learning algorithms

Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015

TL;DR

Authors

References77 items

Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Maize yield and nitrate loss prediction with machine learning algorithms

Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015

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

Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

In-season performance of European Union wheat forecasts during extreme impacts

Machine learning for ecosystem services

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

Machine Learning in Agriculture: A Review

Deep learning in agriculture: A survey

Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology

Benchmarking Machine‐Learning Methods

Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data

Apache Spark

Analysis of Big Data technologies for use in agro-environmental science

Random Forests for Global and Regional Crop Yield Predictions

Advocacy agenda on global Open Data for agriculture and nutrition

Wheat yield prediction using machine learning and advanced sensing techniques

Improving operational maize yield forecasting in Hungary

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.

Definitions and determination of crop yield, yield gaps, and of rates of change

Agricultural production systems modelling and software: Current status and future prospects

Extensive identification and analysis of conserved small ORFs in animals

A scalable satellite-based crop yield mapper

Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level

Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

APSIM - Evolution towards a new generation of agricultural systems simulation

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

Predictive ability of machine learning methods for massive crop yield prediction

Closing yield gaps: perils and possibilities for biodiversity conservation

An Introduction to Statistical Learning

Semantic Compositionality through Recursive Matrix-Vector Spaces

Global food demand and the sustainable intensification of agriculture

Scikit-learn: Machine Learning in Python

A Linked Data representation of the Nomenclature of Territorial Units for Statistics

Remote Sensing of Vegetation: Principles, Techniques, and Applications

引言 (Introduction)

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Greedy function approximation: A gradient boosting machine.

Ridge Regression: Biased Estimation for Nonorthogonal Problems

研究所紹介 米国農務省林産研究所,Atalla博士のグル-プ--Forest Products Laboratory(FPL),Forest Service(FS),United States Department of Agriculture(USDA)

A training algorithm for optimal margin classifiers

WOFOST: a simulation model of crop production.

Principal component analysis

MARS Crop Yield Forecasting System

An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European crop monitoring system

Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe

Seasonal crop yield forecast: Methods, applications, and accuracies

25 years of the WOFOST cropping systems model

An Integrated Crop Yield Model Using Remote Sensing, Agroclimatic Data and Crop Insurance Data

2019. 25 years of the WOFOST

Annual Crop Statistics Handbook

Crop yield predictions-high resolution statistical model for intra-season forecasts applied to corn in the US Gro Intelligence

Analysis of Big Data

NASS and US Crop Production Forecasts: Methods and Issues

Algorithm for the disaggregation of crop area

Introduction to Machine Learning with Python: A Guide for Data Scientists

Deep Learning

Agricultural produc- tion systems

Review of crop yield forecasting methods and early warning systems

The Yield Forecasting Program of NASS. Technical Report

Eurostat

Instance-Based Learning

Self-organization of orientation sensitive cells in the striate cortex

Instance-based learning algorithms

Support-vector networks

Random Forests

Self-Organizing Maps, Third Edition

Machine Learning Approaches

System description of the WOFOST 6.0 crop simulation model implemented in CGMS

Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up

Nearest neighbor pattern classification

JRC Agri4Cast Data Portal

研究所紹介米国農務省林産研究所,Atalla博士のグル-プ--Forest Products Laboratory(FPL),Forest Service(FS),United States Department of Agriculture(USDA)