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Jacome-Galarza L.-R |
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Title |
Crop yield prediction utilizing multimodal deep learning |
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Conference Article |
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Year |
2021 |
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16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
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Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
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Abstract |
La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación. |
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Español |
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cidis @ cidis @ |
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150 |
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Author |
Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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Conference Article |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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cidis @ cidis @ |
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154 |
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Author |
Roberto Jacome Galarza. |
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Title |
Multimodal deep learning for crop yield prediction. |
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Conference Article |
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Year |
2022 |
Publication |
Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. |
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1647 |
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Communicationsin Computer and Infor |
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106-117 |
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cidis @ cidis @ |
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193 |
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Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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Title |
Computer vision for image understanding. A comprehensive review |
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2019 |
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International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
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248-259 |
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Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. |
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gtsi @ user @ |
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97 |
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