Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants

Hyperspectral imaging (HSI) has emerged as a transformative technology in imaging, characterized by its ability to capture a wide spectrum of light, including wavelengths beyond the visible range. This approach significantly differs from traditional imaging methods such as RGB imaging, which uses three color channels, and multispectral imaging, which captures several discrete spectral bands. Through this approach, HSI offers detailed spectral signatures for each pixel, facilitating a more nuanced analysis of the imaged subjects. This capability is particularly beneficial in applications like agricultural practices, where it can detect changes in physiological and structural characteristics of crops. Moreover, the ability of HSI to monitor these changes over time is advantageous for observing how subjects respond to different environmental conditions or treatments. However, the high-dimensional nature of hyperspectral data presents challenges in data processing and feature extraction. Traditional machine learning algorithms often struggle to handle such complexity. This is where 3D Convolutional Neural Networks (CNNs) become valuable. Unlike 1D-CNNs, which extract features from spectral dimensions, and 2D-CNNs, which focus on spatial dimensions, 3D CNNs have the capability to process data across both spectral and spatial dimensions. This makes them adept at extracting complex features from hyperspectral data. In this thesis, we explored the potency of HSI combined with 3D-CNN in agriculture domain where plant health and vitality are paramount. To evaluate this, we subjected lettuce plants to varying stress levels to assess the performance of this method in classifying the stressed lettuce at the early stages of growth into their respective stress-level groups. For this study, we created a dataset comprising 88 hyperspectral image samples of stressed lettuce. Utilizing Bayesian optimization, we developed 350 distinct 3D-CNN models to assess the method. The top-performing model achieved a 75.00\% test accuracy. Additionally, we addressed the challenge of generating valid 3D-CNN models in the Keras Tuner library through meticulous hyperparameter configuration. Our investigation also extends to the role of individual channels and channel groups within the color and near-infrared spectrum in predicting results for each stress-level group. We observed that the red and green spectra have a higher influence on the prediction results. Furthermore, we conducted a comprehensive review of 3D-CNN-based classification techniques for diseased and defective crops using non-UAV-based hyperspectral images.MITACSMaster of Science in Applied Computer Scienc

The Effect of Remineralizing Agents With/Without CO2 Laser Irradiation on Structural and Mechanical Properties of Enamel and its Shear Bond Strength to Orthodontic Brackets

Introduction: Remineralizing agents may be used for the treatment of white spot lesions (WSLs) prior to bracket bonding. However, some concerns exist regarding their possible interference with the etching and bonding process, negatively affecting the bond strength. This study aimed to assess the effect of two remineralizing agents with/without CO2 laser irradiation on the mechanical properties and shear bond strength (SBS) of demineralized enamel to the orthodontic bracket.Methods: This study evaluated 60 premolar teeth in 6 groups (n=10) as follows: (I) sound enamel, (II) demineralized enamel, (III) Nupro remineralizing agent (N), (IV) Nupro and CO2 laser (N/L), (V) Teethmate remineralizing agent (T), and (VI) Teethmate and CO2 laser (T/L). The remineralizing agents were applied to the enamel surfaces after their immersion in a demineralizing solution for 5 days. In groups IV and VI, the CO2 laser with a 10.6 μm wavelength, 10 ms pulse duration, a 50 Hz repetition rate, 0.3 mm beam diameter and 0.7 W power was irradiated after applying the remineralizing agents. Brackets were bonded to the enamel surfaces and SBS was measured by a universal testing machine. For the assessment of enamel microhardness, 20 sections of molar teeth were divided into 4 groups (n=5; N, N/L, T, T/L) and their microhardness was measured before demineralization, after demineralization and after remineralization. X-ray diffraction (XRD) analysis, field-emission scanning electron microscopy (FESEM) and energy-dispersive spectrometry (EDS) were carried out to assess the formation of hydroxyapatite. The atomic percentages of the C, O, P, Ca, Na, Si, F and Ca/P ratio were determined by EDS analysis.Results: The SBS significantly decreased in group II (P < 0.001). There was no significant difference among the groups I, III, IV, V and VI (P < 0.05). This finding was similar to the microhardness results, which showed an increase in microhardness after remineralization (P < 0.05), with no difference among the remineralizing agents. The Ca/P ratio was the highest in the Nupro group and the lowest in the demineralized group.Conclusion: Remineralizing agents can significantly improve the microhardness and structural properties of demineralized enamel to a level similar to that of sound enamel with no adverse effect on SBS to orthodontic brackets

A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral-or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization, before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D and 2D convolutional neural networks (CNNs) struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data."This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors."https://www.sciencedirect.com/science/article/pii/S277237552300145

Efficacy of a Novel Bioactive Glass-Polymer Composite for Enamel Remineralization following Erosive Challenge

INTRODUCTION: Dental caries is the most common cause of tooth loss. However, it can be stopped by enhancing remineralization. Fluoride and casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) are among the most important remineralizing agents. Recent studies have used bioactive glass as a remineralizing agent in different forms. This study aimed to assess the efficacy of a composite paste (prepared by mixing urethane polypropylene glycol oligomer with bioactive glass powder for easier application). MATERIALS AND METHODS: Enamel disks were cut out of the buccal surface of extracted sound third molars. The samples were randomly divided into 3 groups of 15 and underwent Vickers microhardness test. X-ray diffraction (XRD) and field emission scanning electron microscopy/energy dispersive X-ray spectroscopy (FESEM/EDS) were performed. All samples were immersed in a demineralizing solution for 14 days. The tests were then repeated. Next, bioactive glass paste, fluoride, and CPP-ACP were applied on the surface of the samples and they were then stored in an artificial saliva for 14 days. The tests were repeated again. The microhardness values were analyzed using repeated measures ANOVA followed by one-way ANOVA and Tukey's post hoc test (P < 0.05). RESULTS: The microhardness of the bioactive glass group was significantly higher than that of other groups (P < 0.05). XRD revealed an enamel structure more similar to sound enamel in the bioactive glass and CPP-ACP groups compared with the fluoride group. FESEM/EDS revealed higher hydroxyapatite deposition in the bioactive glass group than in the other two groups. CONCLUSIONS: All three remineralizing agents caused remineralization, but bioactive glass paste had a greater efficacy