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

A - Papers appearing in refereed journals

Okyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Simms, D., Mhada, M., Mohareb, F. and Hawkesford, M. J. 2023. Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. Frontiers in Plant Science. 14, p. 1209500. https://doi.org/10.3389/fpls.2023.1209500

AuthorsOkyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Simms, D., Mhada, M., Mohareb, F. and Hawkesford, M. J.
Abstract

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.

KeywordsConvolution neural network; Hyperspectral imaging; Plant nutrition; Machine learning; Spectral curves
Year of Publication2023
JournalFrontiers in Plant Science
Journal citation14, p. 1209500
Digital Object Identifier (DOI)https://doi.org/10.3389/fpls.2023.1209500
Open accessPublished as ‘gold’ (paid) open access
FunderOffice Chérifien des Phosphate (OCP)
Biotechnology and Biological Sciences Research Council
Funder project or codeImage analysis for plant phenotyping - machine learning based methods for analysis of multi-model and multi-dimensional remote sensing data from high-throughput plant phenotyping
Delivering Sustainable Wheat
Publisher's version
Output statusPublished
Publication dates
Online16 Oct 2023
Publication process dates
Accepted05 Sep 2023
PublisherFrontiers Media SA
ISSN1664-462X

Permalink - https://repository.rothamsted.ac.uk/item/98yqw/modeling-the-spatial-spectral-characteristics-of-plants-for-nutrient-status-identification-using-hyperspectral-data-and-deep-learning-methods

29 total views
9 total downloads
1 views this month
1 downloads this month
Download files as zip