Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines

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Year : 2024 | Volume :14 | Issue : 03 | Page : 21-30
By
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Anshik Kushwah,

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Dr. Mayank Pathak,

  1. M. Tech Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology, Anandnagar,Bhopal, Madhya Pradesh,, India
  2. Professor,, Department of Computer Science and Engineering, Technocrats Institute of Technology, Anandnagar,Bhopal,, Madhya Pradesh,, India

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Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of Neural Networks (NN) and Support Vector Machines (SVM), in the identification and classification of mango leaf diseases. Neural Networks excel in extracting complex features from images, while SVMs are robust classifiers, especially in handling non-linear separable data. The hybrid approach leverages the strengths of both models to achieve high accuracy and generalization. This study reviews various models, including ensemble stacked deep learning models, lightweight CNNs, and hybrid CNN-SVM approaches, and compares their effectiveness in detecting diseases like Powdery Mildew, Anthracnose, and others. Results demonstrate that hybrid models outperform traditional methods, achieving accuracy rates as high as 99.87%. However, challenges such as high computational costs, the need for region-specific datasets, and balancing model complexity with accuracy remain. The findings underscore the potential of hybrid models in enhancing disease detection systems, contributing to improved crop management and reduced losses in mango production.

Keywords: Mango Leaf Disease, Neural Networks, Support Vector Machines, Hybrid Models, Machine Learning, Deep Learning, Disease Detection, Agriculture, Image Processing.

[This article belongs to Trends in Opto-electro & Optical Communication (toeoc)]

How to cite this article:
Anshik Kushwah, Dr. Mayank Pathak. Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines. Trends in Opto-electro & Optical Communication. 2024; 14(03):21-30.
How to cite this URL:
Anshik Kushwah, Dr. Mayank Pathak. Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines. Trends in Opto-electro & Optical Communication. 2024; 14(03):21-30. Available from: https://journals.stmjournals.com/toeoc/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 17/10/2024
Accepted 22/10/2024
Published 18/11/2024
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