Improved Plant Disease Detection using Kuwahara Filter and LBP Feature Extraction

Problem Definition

The existing literature reveals several key limitations and challenges in current deep learning methods for identifying plant leaf diseases. While Convolutional Neural Networks (CNN) are widely used, conventional methods are often inefficient. Some studies have focused on enhancing images during preprocessing, but these approaches are limited in only enhancing image contrast rather than overall quality. Additionally, some approaches lack proper feature extraction techniques when dealing with complex data, leading to issues with memory and computation power. This can result in classification algorithms overfitting to training samples and performing poorly on new samples.

In response to these challenges, this paper proposes an efficient model that addresses the shortcomings of traditional methods and provides a solution for feature extraction techniques, ultimately aiming to improve the accuracy and timeliness of plant leaf disease identification.

Objective

The objective of this project is to develop an efficient deep learning model for identifying plant leaf diseases by addressing the limitations of existing methods. The proposed model aims to enhance image quality and contrast using the Kuwahara filter, extract features accurately using the LBP algorithm, and improve classification through Multilayer CNN. By integrating these techniques, the goal is to improve the accuracy and timeliness of plant leaf disease identification.

Proposed Work

From the literature survey, it is evident that existing deep learning methods for plant leaf disease identification have limitations in terms of efficiency and feature extraction techniques. To address these issues, a proposed model is introduced in this project that aims to enhance image quality and contrast using the Kuwahara filter for edge enhancement. Additionally, the LBP feature extraction algorithm is employed to analyze and extract features from the processed images, ensuring accuracy and efficiency in the system. The Multilayer CNN is chosen for classification purposes, providing a robust framework for training and testing the model. The approach involves image acquisition, preprocessing with histogram equalization, edge enhancement with the Kuwahara filter, feature extraction with LBP, and categorization for training and testing.

By integrating these techniques, the proposed model seeks to improve the accuracy and effectiveness of plant leaf disease identification using deep learning methods.

Application Area for Industry

This project can be used in a variety of industrial sectors such as agriculture, pharmaceuticals, and food processing. In agriculture, the automated identification of plant leaf diseases can help farmers in early detection and treatment of diseases, leading to higher crop yields and reduced loss. In pharmaceuticals, the accurate identification of plant leaf diseases can assist in the development of new medicines and treatments. In the food processing industry, the early detection of diseases in plant leaves can ensure the quality of raw materials used in food production. The proposed solutions in this project, including the use of Kuwahara filter for edge enhancement, contrast enhancement techniques, LBP feature extraction algorithm, and Multilayer CNN for classification, can be applied in different industrial domains to address specific challenges.

For example, in agriculture, the use of edge enhancement and contrast enhancement can improve the accuracy of disease identification, while the LBP feature extraction algorithm can help in efficient data analysis. Overall, implementing these solutions can result in benefits such as increased accuracy in disease identification, improved efficiency in data processing, and enhanced quality of raw materials in various industrial sectors.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training by introducing an efficient model for plant leaves disease identification using deep learning methods. The project addresses the limitations of conventional methods by incorporating edge enhancement image processing filters like Kuwahara filter, contrast enhancement techniques, and feature extraction algorithms such as Local Binary Pattern (LBP). This research has the potential to revolutionize the field of plant pathology and image analysis by providing a more accurate and reliable system for identifying plant diseases based on leaf images. By utilizing advanced deep learning techniques like Multilayer CNN for classification, the proposed model can offer enhanced accuracy and efficiency in disease identification. Researchers in the field of computer vision, machine learning, and agricultural science can benefit from this project by exploring innovative research methods, simulations, and data analysis techniques within educational settings.

MTech students and PHD scholars can utilize the code and literature of this project to further their research in image processing, feature extraction, and deep learning algorithms. The technology covered in this project includes edge enhancement filters, feature extraction algorithms, CNN models, and image processing techniques. By applying these technologies, researchers can enhance their research capabilities in developing automated systems for plant disease identification. In conclusion, the proposed project has significant relevance and potential applications in advancing research methods, simulations, and data analysis in the field of plant pathology. Future scope of the project includes expanding the dataset, optimizing the model for real-time disease identification, and exploring more advanced deep learning architectures for improved accuracy and efficiency.

Algorithms Used

In order to enhance the quality and contrast of images of leaves in this project, the following algorithms are utilized: - Kuwahara filter: This edge enhancement image processing filter enhances local discontinuities at the boundaries of different objects in the image, improving the overall quality and contrast. - LBP (Local Binary Pattern): This feature extraction algorithm efficiently extracts features from the processed images, aiding in accurate analysis and classification. - CNN (Convolutional Neural Network): Specifically, a Multilayer CNN is used for classification purposes, offering a more advanced variant of conventional neural networks for improved accuracy. - Histogram equalization: This preprocessing technique is employed to enhance the contrast and quality of the images before applying additional algorithms for further enhancement.

Keywords

SEO-optimized keywords: deep learning methods, plant leaves disease identification, CNN efficiency, image enhancement, preprocessing phases, feature extraction technique, data analysis, classification algorithm, memory usage, computation power, efficient model, feature extraction techniques, traditional models, Edge enhancement filter, Kuwahara filter, contrast enhancement, acutance improvement, LBP feature extraction algorithm, storage efficiency, communication efficiency, retrieval efficiency, Multilayer CNN, image acquisition, Histogram equalization, feature extraction, Local Binary pattern, image categorization, MCNN training, image testing, image preprocessing, Texture feature extraction, image classification, image quality enhancement, disease detection, agricultural technology, plant disease diagnosis, deep learning models, plant health monitoring, plant disease management, agricultural imaging, plant disease detection algorithms, image analysis, agricultural automation.

SEO Tags

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