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ORIGINAL RESEARCH article

Front. Agron.
Sec. Disease Management
Volume 6 - 2024 | doi: 10.3389/fagro.2024.1435234

Unlocking the Potential of Simulated Hyperspectral Imaging in Agro Environmental Analysis: A Comprehensive Study of Algorithmic Approaches

Provisionally accepted
Shafaq Khan Shafaq Khan 1*Munir Majdalawieh Munir Majdalawieh 2Boubakeur Boufama Boubakeur Boufama 1Yajan Sharma Yajan Sharma 1ASHWITHA BASANI ASHWITHA BASANI 1
  • 1 University of Windsor, Windsor, Canada
  • 2 Zayed University, Abu Dhabi, United Arab Emirates

The final, formatted version of the article will be published soon.

    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. Resultsshow 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.

    Keywords: hyperspectral imaging, powdery mildew, deep learning, Plant disease detection, neural networks, Feature extraction techniques, Image Processing in

    Received: 19 May 2024; Accepted: 30 Aug 2024.

    Copyright: © 2024 Khan, Majdalawieh, Boufama, Sharma and BASANI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shafaq Khan, University of Windsor, Windsor, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.