Near real-time monitoring of cassava cultivation area
Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality...
| Main Authors: | , , , , , |
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| Format: | Conference Paper |
| Language: | Inglés |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10568/127367 |
| _version_ | 1855525526295281664 |
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| author | Phan, Trong Van Reymondin, Louis Vantalon, Thibaud Delaquis, Erik Nguyen, Thuy Thanh Mienmany, Bandit |
| author_browse | Delaquis, Erik Mienmany, Bandit Nguyen, Thuy Thanh Phan, Trong Van Reymondin, Louis Vantalon, Thibaud |
| author_facet | Phan, Trong Van Reymondin, Louis Vantalon, Thibaud Delaquis, Erik Nguyen, Thuy Thanh Mienmany, Bandit |
| author_sort | Phan, Trong Van |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality of land-cover mapping (greater precision, and detail in the number of land cover classes). In this work, we propose to use a long short-term memory neural network (LSTM), an advanced technical model adapted from artificial neural networks (ANN) to estimate cassava cultivation area in southern Laos. LSTM is a modified version of a Recurrent Neural Network (RNN) that uses internal memory to store the information received prior to a given time. This property of LSTMs makes them advantageous for time series regression. We employ Landsat-7/8 and Sentinel-2 time-series datasets and crop phenology information to identify and classify cassava fields using multi-sources remote sensing time-series in a highly fragmented landscape. The results indicate an overall accuracy of > 89% for cassava and > 84% for all-class (barren, bush/grassland, cassava, coffee, forest, seasonal, and water) validating the feasibility of the proposed method. This study demonstrates the
potential of LSTM approaches for crop classification using multi-temporal, multi-sources remote sensing time series. |
| format | Conference Paper |
| id | CGSpace127367 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| record_format | dspace |
| spelling | CGSpace1273672025-11-05T11:41:59Z Near real-time monitoring of cassava cultivation area Phan, Trong Van Reymondin, Louis Vantalon, Thibaud Delaquis, Erik Nguyen, Thuy Thanh Mienmany, Bandit cassava forest cover remote sensing machine learning conservación de la naturaleza satélites de observación terrestre mandioca-yuca Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality of land-cover mapping (greater precision, and detail in the number of land cover classes). In this work, we propose to use a long short-term memory neural network (LSTM), an advanced technical model adapted from artificial neural networks (ANN) to estimate cassava cultivation area in southern Laos. LSTM is a modified version of a Recurrent Neural Network (RNN) that uses internal memory to store the information received prior to a given time. This property of LSTMs makes them advantageous for time series regression. We employ Landsat-7/8 and Sentinel-2 time-series datasets and crop phenology information to identify and classify cassava fields using multi-sources remote sensing time-series in a highly fragmented landscape. The results indicate an overall accuracy of > 89% for cassava and > 84% for all-class (barren, bush/grassland, cassava, coffee, forest, seasonal, and water) validating the feasibility of the proposed method. This study demonstrates the potential of LSTM approaches for crop classification using multi-temporal, multi-sources remote sensing time series. 2022-11-26 2023-01-18T09:45:28Z 2023-01-18T09:45:28Z Conference Paper https://hdl.handle.net/10568/127367 en Open Access application/pdf Phan, T.V.; Reymondin, L.; Vantalon, T.; Delaquis, E.; Nguyen, T.T.; Mienmany, B. (2022) Near real-time monitoring of cassava cultivation area. Asian Federation for Information Technology in Agriculture (AFITA) conference 2022, 6th edition: Promoting Smart Technologies for Sustainable Agriculture. 9 p. |
| spellingShingle | cassava forest cover remote sensing machine learning conservación de la naturaleza satélites de observación terrestre mandioca-yuca Phan, Trong Van Reymondin, Louis Vantalon, Thibaud Delaquis, Erik Nguyen, Thuy Thanh Mienmany, Bandit Near real-time monitoring of cassava cultivation area |
| title | Near real-time monitoring of cassava cultivation area |
| title_full | Near real-time monitoring of cassava cultivation area |
| title_fullStr | Near real-time monitoring of cassava cultivation area |
| title_full_unstemmed | Near real-time monitoring of cassava cultivation area |
| title_short | Near real-time monitoring of cassava cultivation area |
| title_sort | near real time monitoring of cassava cultivation area |
| topic | cassava forest cover remote sensing machine learning conservación de la naturaleza satélites de observación terrestre mandioca-yuca |
| url | https://hdl.handle.net/10568/127367 |
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