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Aprendiza= je profundo para predicciones que usan imágenes multiespectrales en agricultura<= /b>

Deep Learning for Multispectral Imaging based Predictions in Agriculture

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Julio Torres Tello

https://orcid.org/0000-0001-9694-6578

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Investigador Independiente, Saskatoon, Canadá.

juliot= orrest@gmail.com

Artículo de Investigación Científica y Tecnológi= ca

Enviado: 22/08/2023

Revisado: 20/09/2023<= o:p>

Aceptado: 02/10/2023<= o:p>

Publicado:13/10/2023<= o:p>

DOI: https://doi.org/10.33262/c= oncienciadigital.v6i4.1.2734

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Cítese:

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Torres Tello, J. (2023). Aprendizaje profundo para predicciones que usan imágene= s multiespectrales en agricultura. ConcienciaDigital, 6(4.1), 75-87. https://d= oi.org/10.33262/concienciadigital.v6i4.1.2734

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CONCIE= NCIA DIGITAL, es una revista multidisciplinar, trimestral, que se publica= rá en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crítica q= ue sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos e= n la sociedad. https://concienciadigital.org<= span lang=3DEN-US style=3D'font-size:8.0pt;line-height:115%;font-family:"Times= New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC'> <= /u>

La rev= ista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec<= /o:p>

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Esta revista está protegida ba= jo una licencia Creative Co= mmons Atribución-NoComercial-C= ompartirIgual 4.0 International. Copia de la licencia: https://creativecommons.or= g/licenses/by-nc-sa/4.0/deed.es <= /o:p>

Palabras claves:

Aprendizaje profundo, Aprendizaje de máquina,
Sensado= remoto, Agricultura,

Multiespectral= ,

Hiperespectral= .

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Resumen

Introducción. La Inteligencia Artifici= al ha logrado un gran éxito en los últimos ańos, y aunque las aplicaciones comercialmente rentables compiten actualmente con los humanos en términos= de precisión y eficiencia, hay otras áreas que podrían beneficiarse de estas tecnologí= as y en las cuales aún existen obstáculos por superar. Uno de los aspectos importantes de este estudio, es que estos resultados nos permiten compren= der de mejor manera las limitaciones relacionadas al uso de datos atípicos en modelos de IA. Esto puede permitir el desarrollo de herramientas para implementar modelos más pequeńos, rápidos, y eficientes con aplicaciones = en la agricultura, y otras áreas que utilicen imágenes = multiespectrales. Objetivo. Se busca proponer un esquema en el cual datos de fuentes= no convencionales y relacionadas a la agricultura, son analizados por modelos personalizados de IA a fin de generar predicciones sobre variables medida= s en el campo, y que eventualmente pueden ayudar al entendimiento de los fenóm= enos físicos y biológicos subyacentes. Metodología. Este trabajo resume= los resultados obtenidos a lo largo de la implementación de un proyecto que ha usado datos de imágenes multi e hiperespectrales de cultivos agrícolas, así como información tomada en el campo. Los conju= ntos de datos incluyen imágenes multiespectrales de cultivos de trigo, e imágenes hiperespectrales de canola y trigo, e incluye mediciones manuales de ciertas variables. En lo= que se refiere a los modelos de IA, estos están estrechamente relacionados al abordaje del problema del procesamiento de datos. En ambos casos se ha op= tado por modelos sencillos de aprendizaje profundo, pero con diferencias en el tipo de datos que estos están optimizados para procesar. Resultados. El principal resultado de este trabajo es la demostración del uso de modelos= de IA/DL para el análisis de datos no convencionales. En el primer caso, usa= ndo redes convolucionales tridimensionales, hemos logrado implementar un modelo que es capaz de predecir el rendimiento de = los cultivos de trigo bajo análisis; y en el segundo caso, usando un esquema dual, con modelos secuenciales y espaciales, hemos logrado realizar la predicción del contenido de humedad. Conclusión. Principalmente, e= ste trabajo demuestra que un modelo DL es capaz de encontrar características útiles dentro de un conjunto de datos MSI para la predicción del rendimie= nto; además de encontrar un modelo DL preciso para la predicción del contenido= de humedad de los cultivos de canola y trigo, basado en HSI. Estos resultados evidencian la versatilidad de los modelos de aprendizaje automático y la posibilidad de extenderlos resultados obtenidos en otras aplicaciones. Área de la ciencia: inteligencia artificial

Keywords:

Deep Learning

Machine Learning

Remote Sensing

Agriculture

Multispectral

Hyperspectral

Abstract

Introduction= . Artificial Intelligence has achieved immense suc= cess in recent years, and although commercially profitable applications curren= tly compete with humans in terms of accuracy and efficiency, there are other areas that could benefit from these technologies and in which obstacles s= till exist. An important aspect of this paper is that these results allow us to better understand the limitations related to the use of uncommon data in = AI models. This may enable the development of tools to implement smaller, faster, and more efficient models with applications in agriculture, and o= ther areas that use multispectral images. Objective. This paper propose= s a scheme in which data from non-conventional sources related to agriculture= are analyzed by custom AI models to generate predictions about variables meas= ured in the fields, and that can eventually help the understanding of the underlying physical and biological phenomena. Methodology. This wo= rk summarizes the results obtained throughout the implementation of a project that has used multi- and hyperspectral image data of agricultural crops, = as well as information taken in the field. The datasets include multispectral images of wheat crops, and hyperspectral images of canola and wheat, and includes manual measurements of certain variables. When it comes to AI models, these are closely related to addressing the problem of data processing. In both cases, simple deep learning models have been chosen, = but with differences in the type of data that they are optimized to process. = Results. The main result of this work is the demonstration of the use of AI/DL models for unconventional data analysis. In the first case, using 3D convolutional networks, we have managed to implement a model that can pre= dict the yield of the wheat crops under analysis; and in the second case, usin= g a dual scheme, with sequential and spatial models, we have managed to predi= ct the moisture content. Conclusion. Primarily, this work demonstrates that a DL model can find useful features within an MSI dataset for yield prediction; in addition to finding an accurate DL model for the predictio= n of moisture content of canola and wheat crops, based on HSI. These results demonstrate the versatility of ML models and the possibility of extending= the results obtained in other applications.

= Introducción

La Inteligencia Artifici= al (IA) ha logrado un gran éxito en los últimos ańos, y aunque las aplicaciones comercialmente rentables, como el reconocimiento de imágenes y chatbots<= /i> compiten actualmente con los humanos en términos de precisión y eficiencia<= /span> (Chollet, 2017), hay otras áreas que podrían beneficiarse de estas tecnologías y en las cuales aún existen obstáculos por superar. Uno de los = problemas actuales para adoptar y adaptar la IA para ayudar a resolver nuevos problem= as es la implementación de estos modelos cuando los conjuntos de datos usados = no son los típicos (imágenes, videos, o texto). Por esto, es necesario implementar, probar, y optimizar modelos de aprendizaje automático (otro de= los nombres de la IA) con conjuntos de datos atípicos (Virnodkar et al., 2020).= En este trabajo se describe la implementación de algunas de estas soluciones, usando imágenes con una cobertura espectral extendida (multi e hiperespectr= ales - MSI y HSI, respectivamente).

Uno de los aspectos importantes de este estudio, es que estos resultados nos permiten comprende= r de mejor manera las limitaciones relacionadas al uso de datos atípicos en mode= los de IA. Esto puede permitir el desarrollo de herramientas para implementar modelos más pequeńos, rápidos, y eficientes con aplicaciones en la agricult= ura, y otras áreas que utilicen imágenes multiespectrales.

Debido a que este articulo compila parte de los resultados de una investigación de varios ańo= s, es importante hacer una breve revisión de los trabajos ya publicados al respecto. En este sentido, tenemos que Identifying<= /i> Useful Features in = Multispectral Images with Deep Learning for Optimizing Wheat Yield Prediction (Torres-Tello & Ko, 2021a) presenta la implementación y uso de modelos personalizados de IA para el análisis de MSI de campos de trigo, tomadas a diferentes intervalos de tiempo. Dicho artículo también explora la posibili= dad de identificar las características más relevantes dentro del conjunto de datos, que usa el modelo para mejorar las predicciones. <= /span>

Por otra parte, A Novel Approach to Identify the Spectral Bandsthat Predict Moisture Co= ntent in Canola and Wheat (Torres-Tello & Ko, 2021b) presenta una metodología para la selección= de las características más relevantes para las predicciones de un modelo de IA, basada en un algoritmo diseńado por Lundberg & Lee (2017), para la explicabilidad / interpretabilidad (SHAP) de este tipo de modelos. Este enfoque se utiliza para analizar qué características generan ciertas predicciones. Una ventaja de la técnica propuesta es que puede preservar el significado físico de las características originales, proporcionando resultados más significativos que los algoritmos de extracci= ón de características como PCA (Xiong et al., 2018). Otro beneficio de esta lí= nea de acción es que puede generar explicaciones del modelo entrenado enfocadas= en un objetivo (por ejemplo, el contenido de humedad), una característica (por ejemplo, espectral o espacial) o un subconjunto de las muestras. Si se nece= sita una explicación global, las contribuciones individuales se pueden agregar e= n un único valor SHAP por característica, debido a su propiedad aditiva (Carlsso= n et al., 2020).

Finalmente, tenemos que Optimizing a Multispectral-Images-Based DL Model, Through <= span class=3DSpellE>Feature Selection, Pruning and Quantization<= /i> (Torres-Tello & Ko, 2022) introduce el uso de los algoritmos de prunin= g y quantization, que son técnicas de optimización de modelos que complementan el enfoque de selección de características y mejoran aún más la tarea de crear modelos eficientes de IA que usan datos no convencionales (Han et al., 2015).

En resumen, este trabajo busca proponer un esquema, como se observa en la figura 1, en el cu= al datos de fuentes no convencionales, y relacionadas a la agricultura, son analizados por modelos personalizados de IA a fin de generar predicciones s= obre variables medidas en el campo, y que eventualmente pueden ayudar al entendimiento de los fenómenos físicos y biológicos subyacentes (Virnodkar = et al., 2020).

Figura 1<= /span>

Esquema general del flujo de trabajo

Metodología

Como se ha mencionado ante= s, este trabajo resume los resultados obtenidos a lo largo de la implementació= n de un proyecto (Torres-Tello, 2022) que ha usado datos de imágenes multi e hiperespectrales de cultivos agrícolas, así como información tomada en el campo. Los conjuntos de datos incluyen 48 imágenes multiespectrales de cult= ivos de trigo, de 12 fechas diferentes en cuatro ubicaciones (dos ubicaciones en= dos ańos diferentes). Cada imagen con 48 parcelas por ubicación, para un total = de 192 muestras, cada una de las cuales contiene información de 12 fechas y 5 bandas espectrales (Torres-Tello & Ko, 2021a). Por otro lado, tenemos imágenes hiperespectrales de canola y trigo, en donde el prim= er grupo consiste en datos de 8 fases de crecimiento de la canola con un total= de 512 muestras, e incluye mediciones manuales de humedad de la vaina, entre otras. Para el caso del trigo, se tiene información de solamente 2 etapas de crecimiento, para un total de 90 muestras (Torres-Tello & Ko, 2021b). A diferencia de lo que sucede en el caso de procesamiento de imágenes tradicionales (con 3 canales), en nuestro caso, un punto importante es el procesamiento de la información espectral (Montesinos-López et al., 2017). Para este cometido, se puede tratar de procesar las imágenes en sus tres dimensiones (alto, ancho, y espectro) al mismo tiempo, o se puede separarla información espacial (alto y ancho) de la espectral, y procesarlas por separado. Dado que en el caso de las imágenes multiespectrales tenemos solamente 5 canales, se ha optado por el primer ca= so; mientras que para las imágenes hiperespectrales, que en nuestro caso tienes= 150 canales, se ha optado por la segunda opción.

En lo que= se refiere a los modelos de IA, estos están estrechamente relacionados al abor= daje del problema del procesamiento de datos. En ambos casos se ha optado por modelos sencillos de aprendizaje profundo (DL – del inglés Deep Learning), pero con diferencias en el tipo de dat= os que estos están optimizados para procesar. Para el caso de las imágenes multiespectrales (Torres-Tello & Ko, 2021a), adoptamo= s un modelo 3D CNN (Convolutional Neural N= etwork), basado en el modelo VGG-16, el cual es un modelo clásico de visión artifici= al y que ha servido de base para varios desarrollos posteriores. Este modelo se escogió, debido a su facilidad para ser modificado y reimplementado; esto debido a que por el tipo de datos, es poco factible usar un modelo pre entr= enado. Es importante mencionar que las redes neuronales convolucionales (CNN) generalmente realizan el procesamiento de imágenes en 2 dimensiones (alto y= ancho), pero en este caso se utilizó una dimensión adicional (tiempo), razón por la cual es una red 3D CNN (Dumoulin & Visin 2018), como se observa en la f= igura 2.

Figura 2<= /span>

Di= agrama de bloques del modelo 3D CNN implementado para el procesamiento de imágenes multiespectrales.

Nota:= Las dimensiones de la = red indican 5 canales espectrales, y la adición del tiempo de la toma de la ima= gen como una dimensión adicional.

Fuente: Torres-Tello & Ko (2022)=

Por otro lado, para el cas= o de las imágenes hiperespectrales, buscamos opciones que den una mayor importan= cia al contenido espectral de los datos. Como se ve en la figura 3, este tipo de imágenes tiene una resolución espectral mucho mayor que otro tipo de imágen= es, lo que quiere decir que el contenido de información suele ser mucho mayor e= n el componente espectral que en el espacial (alto y ancho). <= /a>

Figura = 3=

Las imágenes multi- e hiperespectrales (m= edio y abajo, respectivamente) contienen más información espectral que las imáge= nes RGB (rojo-verde-azul) -arriba-, pero las imágenes hiperespectrales tienen u= na mayor resolución (30 veces más en este caso).<= /p>

La figura 4 muestra el dob= le camino que se implementó para el procesamiento de estas imágenes hiperespectrales. Por una parte, tenemos la información espectral que usa u= na red secuencial (generalmente usada en datos como voz, texto, tendencias, et= c.) LSTM (Long Short-Term Memory), inicialmente propuesta por Hochreiter & Schmidhuber (1997), y que se encarga de relacionar la firma espectral de los cultivos, a sus niveles de humedad. Por otra parte, la información espacial es reducida a 3 canales (Torres-Tello. et al., 2020), para poder ser procesada por una red pre entrenada en el procesamiento de imágen= es RGB. Este esquema nos permite usar toda la información contenida en las imágenes hiperespectrales, a fin de obtenerlos mejores resultados.

Figura = 4=

Esquema general del procesamiento de las imágenes hiperespectrales. Nótese la división en 2 flujos, para procesar la información espacial y espectral, con redes neuronales diferentes.

Fuente: Torres-Tello & Ko (2021b)

Resultados y Discusión
El principal resul= tado de este trabajo es la demostración del uso de modelos de IA/DL para el anál= isis de datos no convencionales. En el primer caso, usando redes convolucionales tridimensionales, hemos logrado implementar un modelo que es capaz de prede= cir el rendimiento de los cultivos de trigo bajo análisis, con un coeficiente de determinación del 0.676, como se observa en la figura 5. =

Figura = 5=

Valores reales y previstos del rendimiento de las parcelas, para tod= o el conjunto de datos de trigo, utilizando imágenes multiespectrales.

=

Fuente: Torres-Tello & Ko (= 2022)

En el caso de la p= redicción del contenido de humedad en cultivos de canola y trigo, usando un esquema d= ual, con modelos secuenciales y espaciales, hemos logrado obtener un coeficiente= de determinación del 0.916 para la canola y 0.818 para el trigo, en lo que se refiere a la predicción del contenido de humedad. =

Estos resultados evidencian la versatilidad de los modelos de aprendizaje automát= ico y la posibilidad de extenderlos resultados obtenidos en aplicaciones comunes, como el procesamiento de imágenes, texto, audio, y video, para aplicaciones más puntuales y que no cuentan con bases = de datos enormes, o grandes capacidades de entrenamiento. Es también interesante el relativo nivel de mejora en los resultados, al usar imágenes hiperespectral= es, que por definición, contienen más información en cada muestra (Manolakis et al., 2016). Es decir, este es un resultado esperado.

Conclusiones<= /b>

· = El principal enfoque y motivación de este trabajo = han sido las oportunidades de inclusión de nuevas tecnologías (como la IA) en l= a agricultura, que todavía presenta muchas oportunidades y necesidades de innovación. Por = lo tanto, estudiamos una forma de utilizar diferentes fuentes de datos obtenid= os principalmente de sitios agrícolas, para implementar modelos de IA personalizados capaces de predecir algunas variables de interés. Principalmente, este trabajo demuestra que un modelo DL es capaz de encontr= ar características útiles dentro de un conjunto de datos MSI para la predicción del rendimiento; además de encontrar un modelo DL preciso para la predicción del contenido de humedad de los cultivos de canola y trigo, basado en HSI.<= o:p>

Conflicto de intereses

Los autor= es declaran que no existe conflicto de intereses.

Referencias Bibliográficas

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= ISSN: 2600-5859

= Vol. 6 No. 4.1, pp. 75 – 87, oc= tubre 2023

www= .concienciadigital.org

Desconexión Digital = Página 75 | 87

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	Artículo de Investigación Científica y Tecnológi= ca Enviado: 22/08/2023 Revisado: 20/09/2023<= o:p> Aceptado: 02/10/2023<= o:p> Publicado:13/10/2023<= o:p> DOI: https://doi.org/10.33262/c= oncienciadigital.v6i4.1.2734
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&= nbsp; Cítese:	&= nbsp;	&= nbsp; Torres Tello, J. (2023). Aprendizaje profundo para predicciones que usan imágene= s multiespectrales en agricultura. ConcienciaDigital, 6(4.1), 75-87. https://d= oi.org/10.33262/concienciadigital.v6i4.1.2734
	&= nbsp;	CONCIE= NCIA DIGITAL, es una revista multidisciplinar, trimestral, que se publica= rá en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crítica q= ue sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos e= n la sociedad. https://concienciadigital.org<= span lang=3DEN-US style=3D'font-size:8.0pt;line-height:115%;font-family:"Times= New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC'> <= /u> La rev= ista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec<= /o:p> <= span style=3D'text-decoration:none'>
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Palabras claves: Aprendizaje profundo, Aprendizaje de máquina, Sensado= remoto, Agricultura, Multiespectral= , Hiperespectral= .	&= nbsp;	Resumen Introducción. La Inteligencia Artifici= al ha logrado un gran éxito en los últimos ańos, y aunque las aplicaciones comercialmente rentables compiten actualmente con los humanos en términos= de precisión y eficiencia, hay otras áreas que podrían beneficiarse de estas tecnologí= as y en las cuales aún existen obstáculos por superar. Uno de los aspectos importantes de este estudio, es que estos resultados nos permiten compren= der de mejor manera las limitaciones relacionadas al uso de datos atípicos en modelos de IA. Esto puede permitir el desarrollo de herramientas para implementar modelos más pequeńos, rápidos, y eficientes con aplicaciones = en la agricultura, y otras áreas que utilicen imágenes = multiespectrales. Objetivo. Se busca proponer un esquema en el cual datos de fuentes= no convencionales y relacionadas a la agricultura, son analizados por modelos personalizados de IA a fin de generar predicciones sobre variables medida= s en el campo, y que eventualmente pueden ayudar al entendimiento de los fenóm= enos físicos y biológicos subyacentes. Metodología. Este trabajo resume= los resultados obtenidos a lo largo de la implementación de un proyecto que ha usado datos de imágenes multi e hiperespectrales de cultivos agrícolas, así como información tomada en el campo. Los conju= ntos de datos incluyen imágenes multiespectrales de cultivos de trigo, e imágenes hiperespectrales de canola y trigo, e incluye mediciones manuales de ciertas variables. En lo= que se refiere a los modelos de IA, estos están estrechamente relacionados al abordaje del problema del procesamiento de datos. En ambos casos se ha op= tado por modelos sencillos de aprendizaje profundo, pero con diferencias en el tipo de datos que estos están optimizados para procesar. Resultados. El principal resultado de este trabajo es la demostración del uso de modelos= de IA/DL para el análisis de datos no convencionales. En el primer caso, usa= ndo redes convolucionales tridimensionales, hemos logrado implementar un modelo que es capaz de predecir el rendimiento de = los cultivos de trigo bajo análisis; y en el segundo caso, usando un esquema dual, con modelos secuenciales y espaciales, hemos logrado realizar la predicción del contenido de humedad. Conclusión. Principalmente, e= ste trabajo demuestra que un modelo DL es capaz de encontrar características útiles dentro de un conjunto de datos MSI para la predicción del rendimie= nto; además de encontrar un modelo DL preciso para la predicción del contenido= de humedad de los cultivos de canola y trigo, basado en HSI. Estos resultados evidencian la versatilidad de los modelos de aprendizaje automático y la posibilidad de extenderlos resultados obtenidos en otras aplicaciones. Área de la ciencia: inteligencia artificial
Keywords: Deep Learning Machine Learning Remote Sensing Agriculture Multispectral Hyperspectral		Abstract Introduction= . Artificial Intelligence has achieved immense suc= cess in recent years, and although commercially profitable applications curren= tly compete with humans in terms of accuracy and efficiency, there are other areas that could benefit from these technologies and in which obstacles s= till exist. An important aspect of this paper is that these results allow us to better understand the limitations related to the use of uncommon data in = AI models. This may enable the development of tools to implement smaller, faster, and more efficient models with applications in agriculture, and o= ther areas that use multispectral images. Objective. This paper propose= s a scheme in which data from non-conventional sources related to agriculture= are analyzed by custom AI models to generate predictions about variables meas= ured in the fields, and that can eventually help the understanding of the underlying physical and biological phenomena. Methodology. This wo= rk summarizes the results obtained throughout the implementation of a project that has used multi- and hyperspectral image data of agricultural crops, = as well as information taken in the field. The datasets include multispectral images of wheat crops, and hyperspectral images of canola and wheat, and includes manual measurements of certain variables. When it comes to AI models, these are closely related to addressing the problem of data processing. In both cases, simple deep learning models have been chosen, = but with differences in the type of data that they are optimized to process. = Results. The main result of this work is the demonstration of the use of AI/DL models for unconventional data analysis. In the first case, using 3D convolutional networks, we have managed to implement a model that can pre= dict the yield of the wheat crops under analysis; and in the second case, usin= g a dual scheme, with sequential and spatial models, we have managed to predi= ct the moisture content. Conclusion. Primarily, this work demonstrates that a DL model can find useful features within an MSI dataset for yield prediction; in addition to finding an accurate DL model for the predictio= n of moisture content of canola and wheat crops, based on HSI. These results demonstrate the versatility of ML models and the possibility of extending= the results obtained in other applications.