STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Image Processing & Pattern Recognition Progress

CNN-based Approach for Efficient Bell Pepper Leaf Disease Recognition

  1. A. Venkata Ramana
  2. K. Ramana
  3. A. Krishna Mohan

Abstract

Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%.) Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%).

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