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Ensuring Privacy Preservation for Various Plants Multi-product Disease Detection and Pesticides Recommendation Data Using Inception V3

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Abstract

Ensuring the privacy and integrity of detected disease images has become a critical concern due to the increasing reliance on deep-learning algorithms for plant disease detection. Existing vulnerabilities in algorithms to content manipulation raise significant risks of inaccurate disease identification, potentially leading to negative impacts on crop health and economics. Moreover, prevailing models often have limited applicability to specific crops, curtailing their use across diverse agricultural contexts. To tackle these issues, this study presents an innovative approach that integrates deep learning methods with the robust secure hash algorithm (SHA)-256 cryptographic algorithm to safeguard disease-detected image privacy. The proposed model is trained on extensive datasets comprising PlantVillage and Fruits&Vegetables, encompassing a wide range of plants, fruits, vegetables, and leaves from the Krishna district. It achieves an impressive 98% accuracy in detecting diseases across diverse plant types using an Inception V3 convolutional neural network architecture. The model gives a unique hash value to each disease-detected image using the SHA-256 method, assuring privacy and preventing unauthorised access or modification. Additionally, the model’s versatility allows it to identify diseases in a wide range of crop categories, including vegetables, fruits, and their corresponding leaves.The study’s novelty lies in its comprehensive approach, merging advanced deep learning techniques with the robust SHA-256 cryptographic algorithm to ensure precise disease detection and data protection. Furthermore, the model provides pesticide recommendations based on identified diseases, thereby decreasing cyber risks in agriculture, protecting crop health, and reducing economic losses caused by erroneous disease detection and pesticide recommendations.

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Data Availability

The data sets were collected from Kaggle.

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Author notes

  1. Rupa Ch, K. Naga Vivek, P. Eswara Chandra and K. Chakradhar have contributed equally to this work.

Authors and Affiliations

  1. Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India

    Rupa Ch, Naga Vivek Karnati, Eswara Chandra Pinjala & Chakradhar Kothapalli

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  1. Rupa Ch
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Correspondence to Rupa Ch.

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This article is part of the topical collection “Diverse Applications in Computing, Analytics and Networks” guest edited by Archana Mantri and Sagar Juneja.

Appendix: Algorithms

Appendix: Algorithms

The Algorithm 1 depicts the entire disease detection method in fruits and vegetables using the Inception V3 CNN model.

Algorithm 1
figure a

Inception V3 CNN Model to detect plant multi-product diseases and recommending pesticides

Full size image

The process for preserving image privacy is illustrated by the Algorithm 2.

Algorithm 2
figure b

Integrity check of image

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Ch, R., Karnati, N., Pinjala, E. et al. Ensuring Privacy Preservation for Various Plants Multi-product Disease Detection and Pesticides Recommendation Data Using Inception V3. SN COMPUT. SCI. 5, 6 (2024). https://doi.org/10.1007/s42979-023-02345-4

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