Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches

Author First name, Last name, Institution

Shafaq Khan, University of Windsor
Munir Majdalawieh, Zayed University
Boubakeur Boufama, University of Windsor
Yajan Sharma, University of Windsor
Ashwitha Basani, University of Windsor

Document Type

Article

Source of Publication

Frontiers in Agronomy

Publication Date

1-1-2024

Abstract

This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity.

DOI Link

10.3389/fagro.2024.1435234

ISSN

2673-3218

Volume

6

Disciplines

Computer Sciences

Keywords

deep learning, feature extraction techniques, hyperspectral imaging, image processing in agriculture, neural networks, plant disease detection, powdery mildew

Scopus ID

85208623481

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Khan, Shafaq; Majdalawieh, Munir; Boufama, Boubakeur; Sharma, Yajan; and Basani, Ashwitha, "Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches" (2024). All Works. 6913.
https://zuscholars.zu.ac.ae/works/6913

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series