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Thumilvannan, S.
- PSO Based Deep Belief Networks Learning for IoT based Crop Disease Detection on Paddy Leaves Using Cloud
Abstract Views :89 |
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 4 (2024), Pagination: 3305-3310Abstract
The Internet of Things (IoT) with advanced machine learning techniques presents significant potential for agricultural applications, particularly in the domain of crop disease detection. Paddy, a staple food for millions, is highly susceptible to various diseases that can drastically affect yield and quality. Early and accurate disease detection is crucial for effective management and mitigation. Traditional methods are often labor-intensive and less reliable, underscoring the need for automated, accurate, and scalable solutions. The primary challenge lies in developing a robust system capable of accurately identifying diseases in paddy leaves using IoT-collected data. This task is complicated by the variability in disease manifestation and environmental conditions, which can affect the quality and consistency of the collected data. Efficient feature extraction and classification techniques are essential to address these issues and ensure high accuracy. This study proposes a novel approach combining Particle Swarm Optimization (PSO) for feature extraction with Deep Belief Networks (DBNs) for classification. IoT devices capture highresolution images of paddy leaves, which are then processed in the cloud. PSO is employed to optimize the feature extraction process by selecting the most relevant features from the image data. These optimized features are fed into a DBN, which is trained to classify the images into healthy or diseased categories. The use of cloud computing ensures the scalability and computational efficiency of the system. The proposed method demonstrates significant improvements in accuracy and processing speed. The PSO-based feature extraction enhances the relevance of features, reducing the dimensionality and improving the DBN's performance. Experimental results show an accuracy rate of 96.3%, with a reduction in processing time by 35% compared to traditional methods. The system's precision and recall rates are 95.8% and 94.7%, respectively, highlighting its effectiveness in real-world applications.Keywords
IoT, Crop Disease Detection, Paddy Leaves, Particle Swarm Optimization, Deep Belief Networks
- Design of Efficient Routing Paths Using Similarity Estimation Based Stochastic Gradient Descent in Wireless Sensor Network
Abstract Views :115 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 3 (2023), Pagination: 2988-2991Abstract
Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.Keywords
Machine Learning, WSN, Stochastic Gradient, Routing, Energy Efficiency.
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- Image Representation and Rendering From Low-Resolution Surveillance Videos Using Densenet
Abstract Views :134 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3155-3160Abstract
In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems.Keywords
Surveillance, Low-Resolution Videos, DenseNet, Image Representation, RenderingReferences
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- Z. Chen and X. Xie, “CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution”, Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 21185-21195, 2023.
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- A Hybrid Machine Learning Approach for Early Detection of Paddy Blight Disease
Abstract Views :88 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3068-3074Abstract
Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.Keywords
Paddy Blight, Disease Detection, Machine Learning, Fuzzy Logic, Deep Learning, Treatment Recommendation.References
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