Newswise — A research team has developed a hyperspectral library for 14 NPK nutrient stress conditions in rice, using a terrestrial hyperspectral camera to collect and analyze 420 rice stress images. The transformer-based deep learning network SHCFTT accurately identified nutrient stress patterns, outperforming SVM, 1D-CNN, and 3D-CNN models with an accuracy ranging from 93.92% to 100%.
This method enhances the precision of nutrient stress detection, contributing to improved crop health monitoring and decision-making in precision agriculture. Rice is a vital crop for global development, but its yield and quality are threatened by various stresses, particularly nutrient stress. Traditional methods for monitoring crop stress are labor-intensive and time-consuming.
While remote sensing technology shows promise, it faces challenges such as atmospheric conditions and mixed farmland communities. Current research highlights the potential of deep learning, particularly Transformer architecture, to enhance hyperspectral imaging (HSI) analysis. However, studies combining deep learning with HSI for identifying rice NPK stress are lacking.
A study (DOI: 10.34133/plantphenomics.0197) published in Plant Phenomics on 29 May 2024, aims to address this gap by developing a deep learning classification network based on CNN and Transformer architecture to accurately identify nutrient stress patterns in rice using terrestrial hyperspectral images.
A research team used HSI collected by SPECIM IQ.