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| DOI | 10.1016/j.jafr.2024.101148 |
| Deep transfer learning for fine-grained maize leaf disease classification | |
| 发表日期 | 2024 |
| ISSN | 2666-1543 |
| 起始页码 | 16 |
| 卷号 | 16 |
| 英文摘要 | Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness and generalizability. In this study, we investigate deep transfer learning for fine-grained disease classification in maize plants, which is a challenging task due to the subtle and nuanced disease patterns. We use four tailored deep learning frameworks: VGGNET, Inception V3, ResNet50, and InceptionResNetV2. ResNet50 achieves the highest validation accuracy of 87.51%, precision of 90.33%, and recall of 99.80%, demonstrating the efficacy and superiority of our approach. Our study offers an innovative solution for accurate disease classification in maize plants. |
| 英文关键词 | Deep transfer learning; Machine learning; Image processing techniques; Disease classification; Maize plants; ChatGPT |
| 语种 | 英语 |
| WOS研究方向 | Agriculture ; Food Science & Technology |
| WOS类目 | Agriculture, Multidisciplinary ; Food Science & Technology |
| WOS记录号 | WOS:001228802900001 |
| 来源期刊 | JOURNAL OF AGRICULTURE AND FOOD RESEARCH
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289937 |
| 作者单位 | Harcourt Butler Technical University (HBTU); VIT Bhopal University; University College of Southeast Norway |
| 推荐引用方式 GB/T 7714 | . Deep transfer learning for fine-grained maize leaf disease classification[J],2024,16. |
| APA | (2024).Deep transfer learning for fine-grained maize leaf disease classification.JOURNAL OF AGRICULTURE AND FOOD RESEARCH,16. |
| MLA | "Deep transfer learning for fine-grained maize leaf disease classification".JOURNAL OF AGRICULTURE AND FOOD RESEARCH 16(2024). |
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