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An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

Author

Listed:
  • Bashar Igried

    (Department of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah II for IT, The Hashemite University, Zarqa 13133, Jordan)

  • Shadi AlZu’bi

    (Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan)

  • Darah Aqel

    (Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan)

  • Ala Mughaid

    (Department of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah II for IT, The Hashemite University, Zarqa 13133, Jordan)

  • Iyad Ghaith

    (Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan)

  • Laith Abualigah

    (Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
    Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
    MEU Research Unit, Middle East University, Amman 11831, Jordan)

Abstract

Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.

Suggested Citation

  • Bashar Igried & Shadi AlZu’bi & Darah Aqel & Ala Mughaid & Iyad Ghaith & Laith Abualigah, 2023. "An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms," Agriculture, MDPI, vol. 13(4), pages 1-20, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:889-:d:1126062
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    References listed on IDEAS

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    1. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    2. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    3. Khanna, Abhishek & Kaur, Sanmeet, 2023. "An empirical analysis on adoption of precision agricultural techniques among farmers of Punjab for efficient land administration," Land Use Policy, Elsevier, vol. 126(C).
    4. Maqableh, Mahmoud & Alia, Mohammad, 2021. "Evaluation online learning of undergraduate students under lockdown amidst COVID-19 Pandemic: The online learning experience and students’ satisfaction," Children and Youth Services Review, Elsevier, vol. 128(C).
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    1. Irtiqa Malik & Muneeb Ahmed & Yonis Gulzar & Sajad Hassan Baba & Mohammad Shuaib Mir & Arjumand Bano Soomro & Abid Sultan & Osman Elwasila, 2023. "Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh," Sustainability, MDPI, vol. 15(14), pages 1-25, July.

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