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Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

Author

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  • Poonam Dhiman

    (Government PG College, Ambala Cantt 133001, India)

  • Amandeep Kaur

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140601, India)

  • Yasir Hamid

    (Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates)

  • Eatedal Alabdulkreem

    (Department of Computer Science, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Hela Elmannai

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Nedal Ababneh

    (Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates)

Abstract

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method.

Suggested Citation

  • Poonam Dhiman & Amandeep Kaur & Yasir Hamid & Eatedal Alabdulkreem & Hela Elmannai & Nedal Ababneh, 2023. "Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4576-:d:1087296
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    References listed on IDEAS

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    1. Zhang, Man & Jin, Yanhong & Qiao, Hui & Zheng, Fengtian, 2017. "Product quality asymmetry and food safety: Investigating the “one farm household, two production systems” of fruit and vegetable farmers in China," China Economic Review, Elsevier, vol. 45(C), pages 232-243.
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    Cited by:

    1. Ammar Kamal Abasi & Sharif Naser Makhadmeh & Osama Ahmad Alomari & Mohammad Tubishat & Husam Jasim Mohammed, 2023. "Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach," Sustainability, MDPI, vol. 15(20), pages 1-18, October.

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