Version 1
: Received: 28 January 2024 / Approved: 29 January 2024 / Online: 29 January 2024 (08:41:15 CET)
How to cite:
Khan, S.; Majdalawieh, M.; Boufama, B.; Sharma, Y.; Basani, A. Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging. Preprints2024, 2024011973. https://doi.org/10.20944/preprints202401.1973.v1
Khan, S.; Majdalawieh, M.; Boufama, B.; Sharma, Y.; Basani, A. Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging. Preprints 2024, 2024011973. https://doi.org/10.20944/preprints202401.1973.v1
Khan, S.; Majdalawieh, M.; Boufama, B.; Sharma, Y.; Basani, A. Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging. Preprints2024, 2024011973. https://doi.org/10.20944/preprints202401.1973.v1
APA Style
Khan, S., Majdalawieh, M., Boufama, B., Sharma, Y., & Basani, A. (2024). Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging. Preprints. https://doi.org/10.20944/preprints202401.1973.v1
Chicago/Turabian Style
Khan, S., Yajan Sharma and Ashwitha Basani. 2024 "Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging" Preprints. https://doi.org/10.20944/preprints202401.1973.v1
Abstract
This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.
Keywords
convolutional neural network (CNN); generative adversarial networks (GANs); gaussian mixture model (GMM); grapevine vein-clearing virus (GVCV); hyperspectral imaging (HSI); normalized difference vegetation index (NDVI); normalized difference water index (NDWI); principal component analysis (PCA); region of interest (ROI); simulated hyperspectral images (SHSI); visual geometry group (VGG-16)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
