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

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 sta

SUBMITTER: Okyere FG 

PROVIDER: S-EPMC10613979 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature