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Author  |
Jacome-Galarza L.-R | ||||
| Title | Crop yield prediction utilizing multimodal deep learning | Type | Conference Article | ||
| Year | 2021 | Publication | 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. | ||||
| 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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| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Español | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 150 | ||
| Permanent link to this record | |||||
| Author |
Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. | ||||
| Title | Time series in sensor data using state of the art deep learning approaches: A systematic literature review. | Type | Conference Article | ||
| Year | 2022 | Publication | VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. | Abbreviated Journal | |
| Volume | 252 | Issue | Pages | 503-514 | |
| Keywords | time series, deep learning, recurrent networks, sensor data, IoT. | ||||
| Abstract | IoT (Internet of Things) and AI (Artificial Intelligence) are becoming support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results. |
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| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 152 | ||
| Permanent link to this record | |||||