Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

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dc.contributor.authorOkyere, Frank Gyan
dc.contributor.authorCudjoe, Daniel
dc.contributor.authorSadeghi-Tehran, Pouria
dc.contributor.authorVirlet, Nicolas
dc.contributor.authorRiche, Andrew B.
dc.contributor.authorCastle, March
dc.contributor.authorGreche, Latifa
dc.contributor.authorSimms, Daniel M.
dc.contributor.authorMhada, Manal
dc.contributor.authorMohareb, Fady
dc.contributor.authorHawkesford, Malcolm John
dc.date.accessioned2023-10-20T09:45:49Z
dc.date.available2023-10-20T09:45:49Z
dc.date.issued2023-10-16
dc.description.abstractSustainable 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.en_UK
dc.identifier.citationOkyere FG, Cudjoe D, Sedaghi-Tehran P, et al., (2023) Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. Frontiers in Plant Science, Volume 14, October 2023, Article number 1209500en_UK
dc.identifier.issn1664-462X
dc.identifier.urihttps://doi.org/10.3389/fpls.2023.1209500
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20414
dc.language.isoenen_UK
dc.publisherFrontiersen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectConvolution Neural Networken_UK
dc.subjecthyperspectral imagingen_UK
dc.subjectPlant Nutritionen_UK
dc.subjectmachine learningen_UK
dc.subjectSpectral curvesen_UK
dc.titleModeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methodsen_UK
dc.typeArticleen_UK

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