DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS

Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stag...
Ausführliche Beschreibung

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Autor*in:	Victor Rueda Ayala [verfasserIn] Seshadri Kunapuli [verfasserIn] Javier Maiguashca [verfasserIn]

Format:	E-Artikel
Sprache:	Spanisch

Erschienen:	2015

Schlagwörter:	aprendizaje automático; ndvi; sensoramiento proximal; sensoramiento remoto; regresión; machine learning; ndvi; proximal sensing; remote sensing; regression

Übergeordnetes Werk:	In: Ecuador es Calidad - Agencia de Regulación y Control Fito y Zoosanitario (AGROCALIDAD), 2020, 2(2015), 1
Übergeordnetes Werk:	volume:2 ; year:2015 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.36331/revista.v2i1.5

Katalog-ID:	DOAJ033774617

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language	Spanish
source	In Ecuador es Calidad 2(2015), 1 volume:2 year:2015 number:1
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topic_facet	aprendizaje automático; ndvi; sensoramiento proximal; sensoramiento remoto; regresión; machine learning; ndvi; proximal sensing; remote sensing; regression Agriculture (General) Animal culture
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container_title	Ecuador es Calidad
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callnumber-first	S - Agriculture
author	Victor Rueda Ayala
spellingShingle	Victor Rueda Ayala misc S1-972 misc SF1-1100 misc aprendizaje automático; ndvi; sensoramiento proximal; sensoramiento remoto; regresión; machine learning; ndvi; proximal sensing; remote sensing; regression misc Agriculture (General) misc Animal culture DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS
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topic_title	S1-972 SF1-1100 DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS aprendizaje automático; ndvi; sensoramiento proximal; sensoramiento remoto; regresión; machine learning; ndvi; proximal sensing; remote sensing; regression
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title	DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS
ctrlnum	(DE-627)DOAJ033774617 (DE-599)DOAJ12f4981d6aca40dbb21ef4af8c052e03
title_full	DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS
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author_browse	Victor Rueda Ayala Seshadri Kunapuli Javier Maiguashca
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title_sort	development of yield prediction models in the maize crop using spectral data for precision agriculture applications
callnumber	S1-972
title_auth	DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS
abstract	Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stages. A model using six degree polynomial regression is recommended for acceptable yield prediction. This model could contribute to decide about imports strategies and avoid the overlapping with the national production.
abstractGer	Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stages. A model using six degree polynomial regression is recommended for acceptable yield prediction. This model could contribute to decide about imports strategies and avoid the overlapping with the national production.
abstract_unstemmed	Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stages. A model using six degree polynomial regression is recommended for acceptable yield prediction. This model could contribute to decide about imports strategies and avoid the overlapping with the national production.
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title_short	DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS
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