Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Ali Hakem Alsaeedi (), Ali Mohsin Al-juboori, Haider Hameed R. Al-Mahmood, Suha Mohammed Hadi, Husam Jasim Mohammed, Mohammad R. Aziz, Mayas Aljibawi and Riyadh Rahef Nuiaa
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Ali Hakem Alsaeedi: College of Computer Science and Information Technology, Al-Qadisiyah University, Diwaniyah 58009, Iraq
Ali Mohsin Al-juboori: College of Computer Science and Information Technology, Al-Qadisiyah University, Diwaniyah 58009, Iraq
Haider Hameed R. Al-Mahmood: Department of Computer Science, College of Science, University of Mustansiriyah, Baghdad 10069, Iraq
Suha Mohammed Hadi: Informatics Institute for Postgraduate Studies, Iraqi Commission for Computer and Informatics, Bagdad 10052, Iraq
Husam Jasim Mohammed: Department of Business Administration, College of Administration and Financial Sciences, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq
Mohammad R. Aziz: College of Computer Science and Information Technology, Al-Qadisiyah University, Diwaniyah 58009, Iraq
Mayas Aljibawi: Computer Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babil 51002, Iraq
Riyadh Rahef Nuiaa: College of Education for Pure Sciences, Wasit University, Wasit 52001, Iraq

Sustainability, 2023, vol. 15, issue 18, 1-20

Abstract: Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

Keywords: olive disease diagnosis; image enhancement; dynamic clustering; deep learning; agricultural applications (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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