使用混合优化进行番茄植物叶病细分和多类疾病检测,实现了深度学习
Manjunatha Badiger1, Jose Alex Mathew2
1Department of Electronics & Communication Engineering, Sahyadri College of Engineering & Management, Mangaluru, Karnataka 575007, India.
Journal of biotechnology
|August 5, 2023
概括
本研究引入了深度学习 (DL) 模型,用于准确检测和分类番茄植物疾病. 开发的技术达到92%以上的准确性,有助于早期发现疾病并提高作物产量.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 植物疾病对全球粮食安全和作物质量构成重大威胁.
- 植物疾病的早期检测和治疗对于可持续农业至关重要.
- 传统的疾病识别方法可能耗时且不太准确.
研究的目的:
- 开发和评估深度学习 (DL) 辅助的技术,用于检测和分类番茄植物疾病.
- 通过使用先进的计算模型,提高植物疾病识别的准确性和效率.
- 通过及时干预植物疾病来提高作物产量和质量.
主要方法:
- 使用深度批量规范化的eLu Alex Net (DbneAlexnet) 模型进行图像分类.
- 采用异构波过来预处理番茄叶的图像以消除扭曲.
- 实现了U-net与渐变黄金搜索优化 (渐变GSO) 进行图像细分.
- 应用图像增强技术,包括位置和颜色增强.
- 训练了DbneAlexnet模型,使用一个拟议的梯度Jaya-Golden搜索优化 (GJ-GSO) 算法.
主要成果:
- 该DL辅助技术在分类番茄植物疾病方面取得了92.4%的高精度.
- 证明了91.9%的真正阳性率 (TPR) 和92.2%的真负率 (TNR).
- 达到0.078的最小假阳性率 (FPR),表明高可靠性.
- 统一的细分和分类方法在识别植物疾病方面证明是有效的.
结论:
- 开发的DL模型,基于GJ-GSO的DbneAlexnet,是识别植物疾病的有效工具.
- 该技术为早期发现疾病提供了显著的好处,有助于改善作物管理.
- 经验研究验证了农业应用开发的模型的有效性和准确性.
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