Predicting yields using biophysical crop simulations, machine learning and remote sensing in India

We tested the innovation and piloted it using ground truth yield data collected by means of crop cutting experiments in Odisha. Before starting to finalize the tool for adoption by insurance providers, the tool will be fine-tuned in the course of 2021.

Bibliographic Details
Main Author: CGIAR Platform for Big Data in Agriculture
Format: Informe técnico
Language:Inglés
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10568/122969
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author CGIAR Platform for Big Data in Agriculture
author_browse CGIAR Platform for Big Data in Agriculture
author_facet CGIAR Platform for Big Data in Agriculture
author_sort CGIAR Platform for Big Data in Agriculture
collection Repository of Agricultural Research Outputs (CGSpace)
description We tested the innovation and piloted it using ground truth yield data collected by means of crop cutting experiments in Odisha. Before starting to finalize the tool for adoption by insurance providers, the tool will be fine-tuned in the course of 2021.
format Informe técnico
id CGSpace122969
institution CGIAR Consortium
language Inglés
publishDate 2020
publishDateRange 2020
publishDateSort 2020
record_format dspace
spelling CGSpace1229692023-03-14T11:47:41Z Predicting yields using biophysical crop simulations, machine learning and remote sensing in India CGIAR Platform for Big Data in Agriculture yields remote sensing development innovation rural development data insurance learning adoption systems agrifood systems experiments machine learning cutting We tested the innovation and piloted it using ground truth yield data collected by means of crop cutting experiments in Odisha. Before starting to finalize the tool for adoption by insurance providers, the tool will be fine-tuned in the course of 2021. 2020-12-31 2022-10-06T14:15:19Z 2022-10-06T14:15:19Z Report https://hdl.handle.net/10568/122969 en Open Access application/pdf CGIAR Platform for Big Data in Agriculture. 2020. Predicting yields using biophysical crop simulations, machine learning and remote sensing in India. Reported in Platform for Big Data in Agriculture Annual Report 2020. Innovations.
spellingShingle yields
remote sensing
development
innovation
rural development
data
insurance
learning
adoption
systems
agrifood systems
experiments
machine learning
cutting
CGIAR Platform for Big Data in Agriculture
Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title_full Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title_fullStr Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title_full_unstemmed Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title_short Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
title_sort predicting yields using biophysical crop simulations machine learning and remote sensing in india
topic yields
remote sensing
development
innovation
rural development
data
insurance
learning
adoption
systems
agrifood systems
experiments
machine learning
cutting
url https://hdl.handle.net/10568/122969
work_keys_str_mv AT cgiarplatformforbigdatainagriculture predictingyieldsusingbiophysicalcropsimulationsmachinelearningandremotesensinginindia