Unlocking the Potential of Simulated Hyperspectral Imaging in Agro Environmental Analysis: A Comprehensive Study of Algorithmic Approaches - MSc Thesis Defense by: Ashwitha Basani

MSc Thesis Defense by: Ashwitha Basani

This study identifies 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 RGB images into SHSIs 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 assess SHSIs for health classification using NDVI values, which are meticulously compared with accurate hyperspectral data using metrics like MAE and 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 SHSIs. These simulated hyperspectral images offer a scalable and affordable approach for the precise assessment of crop disease severity.

Keywords: hyperspectral imaging, powdery mildew, deep learning, plant disease detection, neural networks, feature extraction, image processing.