دورية أكاديمية
Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage
| العنوان: | Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage |
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| المؤلفون: | Sandeep Goshika, Khalid Meksem, Khaled R. Ahmed, Naoufal Lakhssassi |
| المصدر: | International Journal of Molecular Sciences, Vol 25, Iss 1, p 106 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Biology (General) LCC:Chemistry |
| مصطلحات موضوعية: | deep neural networks, soybean leaf damage detection, automatic labeling, computer vision, Biology (General), QH301-705.5, Chemistry, QD1-999 |
| الوصف: | Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1422-0067 1661-6596 |
| العلاقة: | https://www.mdpi.com/1422-0067/25/1/106Test; https://doaj.org/toc/1661-6596Test; https://doaj.org/toc/1422-0067Test |
| DOI: | 10.3390/ijms25010106 |
| الوصول الحر: | https://doaj.org/article/cbcaeef2210a4aba92ea9ba068289cb7Test |
| رقم الانضمام: | edsdoj.bcaeef2210a4aba92ea9ba068289cb7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14220067 16616596 |
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| DOI: | 10.3390/ijms25010106 |