دورية أكاديمية
An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model.
| العنوان: | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model. |
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| المؤلفون: | Ullah N; Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan., Khan JA; Department of Computer Science, Faculty of Physics, Engineering, and Computer Science, University of Hertfordshire, Hatfield, United Kingdom., Almakdi S; Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia., Alshehri MS; Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia., Al Qathrady M; Departments of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia., El-Rashidy N; Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kaferelshikh University, Kafr El-Shaikh, Egypt., El-Sappagh S; Faculty of Computer Science and Engineering, Galala University, Suez, Egypt.; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt., Ali F; Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea. |
| المصدر: | Frontiers in plant science [Front Plant Sci] 2023 Oct 11; Vol. 14, pp. 1212747. Date of Electronic Publication: 2023 Oct 11 (Print Publication: 2023). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Lausanne : Frontiers Research Foundation, 2010- |
| مستخلص: | Introduction: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Ullah, Khan, Almakdi, Alshehri, Al Qathrady, El-Rashidy, El-Sappagh and Ali.) |
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| فهرسة مساهمة: | Keywords: DeepPlantNet; artificial intelligence; deep learning; leaf diseases; plant diseases classification |
| تواريخ الأحداث: | Date Created: 20231030 Latest Revision: 20231031 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC10600380 |
| DOI: | 10.3389/fpls.2023.1212747 |
| PMID: | 37900756 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1664-462X |
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| DOI: | 10.3389/fpls.2023.1212747 |