Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina

Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progre...

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Detalles Bibliográficos
Autores principales: Sahajpal, Ritvik, Fontana, Lucas, Lafluf, Pedro, Leale, Guillermo, Puricelli, Estefania, O’Neill, Dan, Hosseini, Mehdi, Varela, Mauricio, Reshef, Inbal
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2020
Materias:
Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/115530
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-115530
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
spellingShingle Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
Sahajpal, Ritvik
Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
topic_facet Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
description Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.
format Objeto de conferencia
Objeto de conferencia
author Sahajpal, Ritvik
Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
author_facet Sahajpal, Ritvik
Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
author_sort Sahajpal, Ritvik
title Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_short Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_full Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_fullStr Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_full_unstemmed Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_sort using machine-learning models for field-scale crop yield and condition modeling in argentina
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/115530
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