在使用基于神经网络的模型来增加农业生产力的番茄植物中早期和晚期虫病的识别
Muzammil Khan1, Fahmida Gulan2, Muhammad Arshad2
1Department of Computer & Software Technology, University of Swat, KP, Pakistan.
Science progress
|September 12, 2024
概括
这项研究引入了一个深度学习模型,用于准确识别番茄叶病,达到97.44%的准确性. 这项技术有助于农民在早期检测,提高作物产量和经济稳定.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 在全球范围内,番茄种植在经济上至关重要,年产量达到1.6亿.
- 番茄作物面临着巨大的产量损失 (高达79%) 由于疾病,如早期和晚期.
- 传统的疾病诊断方法是劳动密集的,昂贵的,需要专家知识.
研究的目的:
- 提高番茄叶病识别的准确性和效率.
- 开发一种基于深度学习的解决方案,用于自动化疾病分类.
- 减轻番茄植物疾病对农业生产力的经济影响.
主要方法:
- 开发了一个使用 inception-V3 架构的深度学习模型.
- 用于培训和测试的是包含6000张西红叶图像的PlantVillage数据集.
- 80:20数据分割用于模型培训和验证.
主要成果:
- 开始-V3模型在识别番茄叶病时实现了97.44%的高分类准确度.
- 该模型在区分健康和患病的西红叶子方面表现出有效性.
- 拟议的方法比传统的诊断技术提供了显著的改进.
结论:
- 深度学习模型,特别是 inception-V3,对准确高效地检测番茄病有很大的前景.
- 这种自动化方法可以支持农民及时管理疾病,从而提高作物产量.
- 这项技术的成功实施可以加强番茄产业的经济可行性和全球竞争力.
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