A Model Proposal for Enhancing Leaf Disease Detection Using Convolutional Neural Networks (CNN)

Case Study

Authors

  • Moulay Hafid Aabidi Multidisciplinary Research Laboratory for Science, Technology and Society, Department of Computer Engineering and Mathematics, Higher School of Technology, Khenifra Sultan Moulay Slimane University, Morocco
  • Adil EL Makrani Computer Science Research Laboratory, Faculty of Science, Ibn tofail University Kenitra, Morocco
  • Brahim Jabir LIMATI Laboratory of Polydisciplinary Faculty https://orcid.org/0000-0002-8762-9199
  • Imane Zaimi Multidisciplinary Research Laboratory for Science, Technology and Society, Department of Computer Engineering and Mathematics, Higher School of Technology, Khenifra Sultan Moulay Slimane University, Morocco

DOI:

https://doi.org/10.3991/ijoe.v19i12.40329

Keywords:

Deep learning, CNN Models, Computer Vision, VGG, Leaf Disease Detection

Abstract


Deep learning has gained significant popularity due to its exceptional performance in various machine learning and artificial intelligence applications. In this paper, we propose a comprehensive methodology for enhancing leaf disease detection using Convolutional Neural Networks (CNNs). Our approach leverages the power of CNNs and introduces innovative techniques to improve accuracy and provide insights into the inner workings of the models. The methodology encompasses multiple stages. We describe the methodology as follows: Firstly, we employ advanced preprocessing techniques to enhance the leaf image dataset, including data augmentation methods to augment the training data and improve model accuracy. Secondly, we design and implement a robust Convolutional Neural Network architecture with multiple layers and ReLU activation, enabling the network to effectively learn complex patterns and features from the input images. To facilitate monitoring and control of the CNN processes, we introduce a novel network visualization module. This module offers a filter-level 2D embedding view, providing real-time insights into the inner workings of the network and aiding in the interpretation of the learned features. Additionally, we develop an interactive module that enables real-time model control, allowing researchers and practitioners to fine-tune the model parameters and optimize its performance. To evaluate the effectiveness of our proposed methodology, we conduct extensive experiments using the PlantVillage dataset, which contains a diverse range of plant diseases captured through a large number of leaf images. Through rigorous analysis and evaluation, we demonstrate the superior performance of our approach, achieving classification accuracy exceeding 99%.

Downloads

Published

2023-08-31

How to Cite

Aabidi, M. H., EL Makrani , A. ., Jabir, B., & Zaimi, I. . (2023). A Model Proposal for Enhancing Leaf Disease Detection Using Convolutional Neural Networks (CNN): Case Study. International Journal of Online and Biomedical Engineering (iJOE), 19(12), pp. 127–143. https://doi.org/10.3991/ijoe.v19i12.40329

Issue

Section

Papers