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基于最佳转移学习的营养缺乏分类模型在 (Luffa acutangula) 中
Finney Daniel Shadrach1, Gunavathi Kandasamy2, S Neelakandan3
1Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India.
Scientific reports
|August 29, 2023
概括
对于农民来说,检测 (Luffa acutangula) 中的营养缺乏是非常重要的. 使用Ring Toss游戏优化与深度转移学习 (RTGODTL-NDC) 的新方法准确地识别了这些缺陷,改善了作物管理.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 诸如 (Luffa acutangula) 等作物的营养缺乏给农民带来了重大挑战,影响了产量和质量.
- 准确及早发现这些缺陷对于有效的作物管理和可持续农业至关重要.
研究的目的:
- 开发和评估一种新的方法,用于对山脊植物的营养不足进行分类.
- 通过改善作物健康监测和有针对性的干预措施,加强农业实践.
主要方法:
- 使用基于深度转移学习的营养素缺陷分类 (RTGODTL-NDC) 框架实现环投游戏优化.
- 使用Gabor过器进行图像预处理,使用SqueezeNet模型与Adam优化器进行特征提取,以及使用深度混合学习模型进行分类.
主要成果:
- RTGODTL-NDC方法在分类营养缺乏方面取得了高性能.
- 在识别山脊植物的营养缺乏方面,准确度达97.16%和特异性达98.29%.
结论:
- RTGODTL-NDC框架提供了一个可靠的解决方案,用于准确检测山脊的营养缺乏.
- 这种方法有可能显著改善作物管理,从而改善农业健康和资源利用.
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