基于有效特征选择的HOBS修剪-ELM模型用于番茄植物叶病的分类
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nādu, India.
PloS one
|December 5, 2024
概括
一个新的轻量级深度学习模型准确地实时分类番茄植物疾病. 这种优化的卷积神经网络 (CNN) 显著减少了处理时间和模型大小,优于现有的方法,提高了作物产量潜力.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由于生物和非生物压力,番茄种植面临着显著的收获损失.
- 传统的深度学习模型用于植物疾病分类是计算密集的,阻碍了实时应用.
- 现有的方法在复杂的农业环境中往往难以提高效率和准确性.
研究的目的:
- 开发一种轻量级的卷积神经网络 (CNN),用于实时对番茄叶的生物应激分类.
- 解决传统CNN的局限性,专注于减少处理复杂性和提高准确性.
- 创建一个有效的模型,用于早期检测番茄植物的疾病,这对可持续农业至关重要.
主要方法:
- 提出了一种新的轻量级CNN架构,将象群优化 (EHO) 纳入功能选择中.
- 集成的基于Hessian的最佳脑外科医生 (HOBS) 带有一个修剪的极端学习机器 (ELM) 进行参数优化.
- 利用植物村数据集,在10个番茄植物类别中使用8,000张图像进行培训和验证.
主要成果:
- 获得了95.73%的高精度和0.81%的科恩卡帕,训练时间为2.35秒.
- 与传统模型相比,修剪后的模型显示了9.2 MB的缩小尺寸和更少的参数.
- 超越了现有的模型,如修剪的DenseNet (86.64%准确率,10.6 MB),GhostNet (92.15%准确率,10.9 MB) 和CACPNET (92.4%准确率,18.0 MB).
结论:
- 开发的轻量级CNN框架为实时番茄植物疾病分类提供了卓越的准确性和处理效率.
- 优化技术显著降低了计算复杂性,使该模型适合实际的现场应用.
- 这种方法有望加强疾病管理策略,并通过早期和准确的检测改善番茄作物产量潜力.
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