A Novel Deep Learning Approach for Anthracnose Detection in Mango Leaves

Problem Definition

The field of plant disease detection has seen significant progress in recent years, with the integration of deep learning methodologies leading the way. However, several key limitations and challenges have surfaced, calling for the development of more sophisticated models. One of the primary issues in this domain is the large number of variations present among different types of plants and leaves, making it difficult to standardize detection procedures. Furthermore, the lack of a defined structure or shape associated with infected leaf regions poses a significant obstacle to accurate detection. Current solutions proposed by various authors have also fallen short in terms of recognition rates, highlighting the need for an automated and efficient technique to address these shortcomings.

These challenges underscore the necessity for a new approach in the field of plant disease detection to enhance agricultural production and sustainability.

Objective

The objective of this research is to develop a novel approach for plant disease detection by addressing the limitations in existing models. Specifically, the focus is on enhancing agricultural production through the introduction of a histogram equalization technique (MMBEBHE) for image enhancement and a MCNN-based ternary classification model for detecting and classifying disease in mango leaves. By overcoming challenges such as variations in plant types, undefined structures of infected areas, and low recognition rates in current solutions, this proposed work aims to improve the accuracy and efficiency of plant disease detection to benefit agricultural sustainability.

Proposed Work

The research on plant disease detection has shown significant progress, but certain limitations in existing models have highlighted the need for a new approach. Deep learning has become increasingly popular in agriculture, emphasizing the importance of technology in enhancing agricultural production. Challenges such as variations in plant types, undefined structures or shapes of infected areas, and low recognition rates in current solutions have prompted the development of a novel model. The proposed work aims to address these challenges by introducing a histogram equalization technique called MMBEBHE for image enhancement and a MCNN-based ternary classification model for detecting and classifying disease in mango leaves. Classification of plant diseases through image segmentation has become a common practice, with CNNs being a popular choice for such tasks.

The current model for classifying diseased mango leaves has a complex structure with multiple layers for processing information, yet it still has limitations that need to be overcome. The new model focuses on detecting Anthracnose, a fungal disease, and incorporates the MMBEBHE technique to improve image quality by preserving brightness, removing noise, enhancing the image, and maintaining background colors. Additionally, the use of region of interest (ROI) instead of the central square crop method helps extract essential information by detecting edges. Preprocessing images with an HE approach ensures the validity of the proposed model, which will be trained and tested using a MCNN-based ternary classification model to identify diseases effectively.

Application Area for Industry

This project can be utilized in various industrial sectors such as agriculture, horticulture, and food production. The proposed solutions address the challenges faced in plant disease detection, including the large number of variations across different types of plants and leaves, lack of defined structure in infected leaf regions, and low recognition rates with current solutions. Implementing the novel model for classifying diseased mango leaves with the introduction of the MMBEBHE histogram equalization technique and ROI extraction will lead to benefits such as improved image brightness preservation, noise removal, enhanced image quality, and better preservation of background colors. The effectiveness of the model will be validated through HE preprocessing and training a MCNN based ternary classification model, providing industries with an efficient and accurate tool for detecting plant diseases and enhancing agricultural production.

Application Area for Academics

The proposed project on the classification of plant diseases using image segmentation and deep learning techniques has the potential to greatly enrich academic research, education, and training in the field of agricultural science. By addressing the challenges faced in the existing models and introducing novel methodologies such as MMBEBHE and ROI extraction, the project offers a significant contribution to the advancement of research methods in plant disease detection. Academically, researchers, MTech students, and PhD scholars focusing on agricultural science and image processing can benefit from the code and literature of this project. The utilization of CNN, MMBEBHE, and ROI extraction algorithms provides a valuable resource for exploring innovative research methods and simulations in the field of plant disease detection. The novel model proposed in this project opens up possibilities for further exploration and experimentation in the domain of agricultural production and disease control.

Furthermore, the project's focus on improving the accuracy and efficiency of disease detection through deep learning models can equip researchers and students with valuable skills in data analysis and image processing techniques. The application of the proposed model in identifying Anthracnose disease in Mango leaves showcases its relevance and potential impact on agricultural research and production. In conclusion, the proposed project on plant disease classification using deep learning and image segmentation techniques offers a comprehensive approach to addressing the limitations of existing models. The integration of advanced algorithms and methodologies in this project can serve as a valuable resource for researchers and students seeking to enhance their knowledge and skills in agricultural science and data analysis. Reference Future Scope: Future research in this area could focus on expanding the application of the proposed model to other types of plant diseases and crops.

The development of more sophisticated deep learning models and algorithms for disease detection could further improve the accuracy and efficiency of plant disease classification in agricultural settings. Additionally, exploring the integration of IoT technologies for real-time disease monitoring and control could offer new avenues for innovation in the field of agricultural science.

Algorithms Used

The algorithms used in this project are MMBEBHE (Minimum Mean Brightness Error Bi-Histogram Equalization), ROI extraction, and CNN (Convolutional Neural Network). MMBEBHE is utilized for preserving image brightness, removing noise, enhancing image quality, and preserving background colors effectively. It plays a crucial role in preprocessing the images for disease classification. ROI extraction is implemented to extract essential information from the images by detecting edges. This method replaces the central square crop technique and improves the accuracy of disease detection.

CNN, a deep learning model, is employed for classifying plant diseases by analyzing segmented images. In this project, CNN is used to train a ternary classification model for identifying the Anthracnose disease in mango leaves. The novel model architecture aims to address the limitations of existing models and achieve more accurate disease classification results.

Keywords

SEO-optimized keywords: plant diseases detection, deep learning, agriculture, image segmentation, CNN, classification model, mango leaves, Anthracnose, histogram equalization, MMBEBHE technique, image enhancement, noise removal, region of interest, edge detection, validation, HE approach, MCNN, ternary classification model, fungal disease classification, machine learning, image analysis, agriculture, plant pathology, accuracy enhancement, data preprocessing, disease identification, model evaluation.

SEO Tags

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