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Crop leaf disease detection for beans using ensembled-convolutional neural networks

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Published/Copyright: October 5, 2023
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International Journal of Food Engineering
From the journal International Journal of Food Engineering Volume 19 Issue 11

Abstract

Crops’ health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs’ has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs.

Keywords: deep learning; crop disease detection; ensembling; images; convolutional neural network; disease symptoms
Corresponding author: Priyanka Sahu, University School of Information, Communication, and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India; and Galgotias College of Engineering & Technology, Greater Noida, U.P., India, E-mail:

Acknowledgments

Authors are thankful to the Department of Science & Technology, Government of India, Delhi, for funding a project on “Application of IoT in Agriculture Sector” through the ICPS division. This work is a part of the project work.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: Department of Science & Technology, Government of India, Delhi.

  5. Data availability: Not applicable.

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Received: 2023-02-23
Accepted: 2023-09-14
Published Online: 2023-10-05

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Articles
  3. Application and evaluation of short-wave infrared radiation for peanut oil production
  4. Effect of fermentation of Danisco mixed lactic acid bacteria and single Bifidobacterium lactis HCS04-001 on metabolites of soy yogurt
  5. Crop leaf disease detection for beans using ensembled-convolutional neural networks
  6. Discrimination of powdered herbal teas by Vis/NIR spectral reflectance and chemometrics
  7. The production of aflatoxin B1 by Aspergillus parasiticus in peanuts and walnuts under the influence of controlled temperature and water activity
  8. Physico-functional and quality attributes of microwave-roasted black pepper (Piper nigrum L.)
https://doi.org/10.1515/ijfe-2023-0055
Keywords for this article
deep learning; crop disease detection; ensembling; images; convolutional neural network; disease symptoms

Articles in the same Issue

  1. Frontmatter
  2. Articles
  3. Application and evaluation of short-wave infrared radiation for peanut oil production
  4. Effect of fermentation of Danisco mixed lactic acid bacteria and single Bifidobacterium lactis HCS04-001 on metabolites of soy yogurt
  5. Crop leaf disease detection for beans using ensembled-convolutional neural networks
  6. Discrimination of powdered herbal teas by Vis/NIR spectral reflectance and chemometrics
  7. The production of aflatoxin B1 by Aspergillus parasiticus in peanuts and walnuts under the influence of controlled temperature and water activity
  8. Physico-functional and quality attributes of microwave-roasted black pepper (Piper nigrum L.)
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