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Detection of plant leaf diseases using deep convolutional neural network models

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Abstract

Food demand is exponentially increasing due to the increase in population in every country; hence, increasing the yield is one of the focus areas for sustainable agricultural development. Predicting plant disease is one of the measures to increase crop yield and quality, thereby increasing the economy. The present work aims to develop a web-based application built with a deep learning model to detect plant leaf disease using a leaf image and alert farmers with messages. A comparative study was conducted on the data of the PlantVillage dataset for binary and multiclass classifications. Various deep convolutional neural network (CNN) models, such as MobileNet, DenseNet201, ResNet50, Inception V3, and visual geometry group (VGG) 16 and 19, have been compared with a proposed model. Various metrics include precision, recall, classification report, Confusion Matrix, and accuracy. MobileNet is influential among the selected models, with an accuracy of 97.35% and precision and recall of 0.973 each for multiclass classification. The proposed model achieved an accuracy of 99.39% with a loss of 0.0361, precision of 0.989, and a recall of 0.984 for binary classification compared with deep CNN models. A web-based application was created using the MobileNet model for the convenience of sending an email alert to the user regarding plant disease. The research results help improve a country's crop productivity and the overall economy through prompt and precise decision-making on crop diseases.

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Data availability

Data will be made available on request.

Abbreviations

CNN:

Convolutional neural network

FN:

False Negative

FP:

False Positive

kNN:

K- Nearest Neighbors

TN:

True Negative

TP:

True Positive

VGG:

Visual geometry group

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Acknowledgements

The authors thank the SRM Institute of Science and Technology, Kattankulathur, Chennai, India, for providing the required research infrastructure.

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No funding was received.

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Authors and Affiliations

  1. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    Puja Singla & Vijaya Kalavakonda

  2. Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    Ramalingam Senthil

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  1. Puja Singla
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  2. Vijaya Kalavakonda
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  3. Ramalingam Senthil
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Contributions

Puja Singla: Conceptualization, Methodology, Design, Data collection, Data Visualization, Formal analysis, Vijaya Kalavakonda: Conceptualization, Methodology, Design, Investigation. Data collection, Data analysis, Writing- Original draft preparation, Ramalingam Senthil: Methodology, Data analysis, Data curation, Formal analysis, Writing, Writing–review & editing.

Corresponding author

Correspondence to Vijaya Kalavakonda.

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Resources

The related data are discussed in the manuscript and the relevant resources are provided as follows.

Data: https://github.com/pujasingla/Plant-Village-Dataset

Codes: Binary Classification: https://github.com/pujasingla/Plant-Disease-Detection-Binary-Classification

Multi Classification/Web Application: https://github.com/pujasingla/Plant_LeafDiseaseDetection

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Singla, P., Kalavakonda, V. & Senthil, R. Detection of plant leaf diseases using deep convolutional neural network models. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-18099-3

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