Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality...
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Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea
The work presented in this paper has great significance in improving electromagnetic models based on the strong coupling between the magnetic and electric fields transient equations while considering a realistic rando...
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The increasing adoption of PV-battery systems highlights the need for accurately assessing their economic feasibility, particularly in the absence of feed-in tariff and net metering schemes that are currently abandone...
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A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time dat...
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Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant he...
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Wetlands are critical ecosystems providing numerous ecological services, yet they face significant threats from human activities and climate change. Therefore, accurate mapping and monitoring of wetlands are crucial f...
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A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, ...
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Autonomous aerial vehicles (AAVs) with wireless power transfer (WPT) technology offer a promising solution to extend the lifetime of wireless rechargeable sensor networks (WRSNs) by swiftly recharging multiple sensor ...
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