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Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise i...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613979/ https://www.ncbi.nlm.nih.gov/pubmed/37908836 http://dx.doi.org/10.3389/fpls.2023.1209500 |
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author | Okyere, Frank Gyan Cudjoe, Daniel Sadeghi-Tehran, Pouria Virlet, Nicolas Riche, Andrew B. Castle, March Greche, Latifa Simms, Daniel Mhada, Manal Mohareb, Fady Hawkesford, Malcolm John |
author_facet | Okyere, Frank Gyan Cudjoe, Daniel Sadeghi-Tehran, Pouria Virlet, Nicolas Riche, Andrew B. Castle, March Greche, Latifa Simms, Daniel Mhada, Manal Mohareb, Fady Hawkesford, Malcolm John |
author_sort | Okyere, Frank Gyan |
collection | PubMed |
description | Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages. |
format | Online Article Text |
id | pubmed-10613979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106139792023-10-31 Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods Okyere, Frank Gyan Cudjoe, Daniel Sadeghi-Tehran, Pouria Virlet, Nicolas Riche, Andrew B. Castle, March Greche, Latifa Simms, Daniel Mhada, Manal Mohareb, Fady Hawkesford, Malcolm John Front Plant Sci Plant Science Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages. Frontiers Media S.A. 2023-10-16 /pmc/articles/PMC10613979/ /pubmed/37908836 http://dx.doi.org/10.3389/fpls.2023.1209500 Text en Copyright © 2023 Okyere, Cudjoe, Sadeghi-Tehran, Virlet, Riche, Castle, Greche, Simms, Mhada, Mohareb and Hawkesford https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Okyere, Frank Gyan Cudjoe, Daniel Sadeghi-Tehran, Pouria Virlet, Nicolas Riche, Andrew B. Castle, March Greche, Latifa Simms, Daniel Mhada, Manal Mohareb, Fady Hawkesford, Malcolm John Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title | Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title_full | Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title_fullStr | Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title_full_unstemmed | Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title_short | Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
title_sort | modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613979/ https://www.ncbi.nlm.nih.gov/pubmed/37908836 http://dx.doi.org/10.3389/fpls.2023.1209500 |
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