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Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks

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Plant and Soil Aims and scope Submit manuscript

Abstract

Aims and background

Pests and diseases of plants often threaten the availability and safety of plants for human consumption. To face these challenges, a new agricultural revolution is underway (agriculture 4.0). This agrarian revolution dramatically benefits from new digital technologies and artificial intelligence (AI).

Methods

The farmers need a reliable tool for an early disease diagnosis. Imaging is a promising technique for diagnosing and quantifying the disease plot. Easily automated and non-intrusive, imaging allows, with low costs in instrumentation and human resources, to account for much agricultural priority’s local mics on large production areas. The main purpose paper is to develop a hybrid model for tomato disease detection based on image data collection. We apply transfer learning and fine-tuning strategies to improve the performance of different pre-trained models. Two models have been selected to develop our hybrid model for plant disease identification among these CNN models. We used the plant village dataset, which contains nine classes of tomato diseases.

Results

First, we evaluate the performance of seven different architectures including VGG16, ResNet50, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3 and EfficientNetB4. We applied the transfer learning technique. Then, the best two pre-trained models were selected and used to implement a weighted average ensemble. The proposed model achieves an accuracy of 0.981.

Conclusion

Many diseases can affect tomato plants and cause yield losses. Therefore, plant pathogens should be given more importance. Furthermore, this study can be adapted to cover other types of crops in future research.

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Data availability

Data is available from its original source as cited in the article. The PlantVillage dataset is available at https://www.kaggle.com/datasets/charuchaudhry/plantvillage-tomato-leaf-dataset

Abbreviations

AI:

Artificial intelligence

ML:

Machine learning

SVM:

Support vector machines

LR:

Logistic Regressions,

KNN:

K-Nearest Neighbors

DT:

Decision Trees

RF:

Random forest

NB:

Naive Bayes

LDA:

Linear discriminant analysis

HOG:

Histogram of oriented gradients

PLS:

Partial least squares

ANN:

Artificial neural network

CNN:

Convolutional neural network

VGG-16:

Visual geometry group-16

TP:

True Positive

TN:

True Negative

FN:

False Negative

FP:

False Positive

TinyML:

Tiny machine learning

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. SIRC- (LaGeS), Hassania School of Public Works, Casablanca, Morocco

    Mariam Moussafir, Rachid Saadane & Abdessamad El Rharras

  2. GENIUS Laboratory, SUPMTI, Hassan-Rabat, Morocco

    Hasna Chaibi

  3. Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada

    Abdellah Chehri

  4. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Gwanggil Jeon

  5. Department of Embedded Systems Engineering, Incheon National University, Incheon, 22012, South Korea

    Gwanggil Jeon

Authors
  1. Mariam Moussafir
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  2. Hasna Chaibi
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  4. Abdellah Chehri
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  5. Abdessamad El Rharras
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  6. Gwanggil Jeon
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Contributions

M.M participated in the study's experimental design and grazing experiment; M.M, H.C, R.S and A. C performed the statistical analysis. M.M and H. C, R. S, A. C, and A. E-R completed the methodology, the validation, formal analysis, and investigation. M. M prepared the draft for the manuscript. The editing was completed by A. C, and G. J. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gwanggil Jeon.

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Responsible Editor: Muhammad Tariq.

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Moussafir, M., Chaibi, H., Saadane, R. et al. Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks. Plant Soil 479, 251–266 (2022). https://doi.org/10.1007/s11104-022-05513-2

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