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苗生长阶段的智能识别方法及其在激光补充照明控制研究中的应用
Xiaoyan Liu1,2, Leijinyu Zhou1, Bei Jiang1
1College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
Scientific reports
|November 13, 2025
概括
这项研究介绍了PGL-ShuffleNetV2,这是一种轻量级的深度学习模型,用于准确识别大米苗种阶段. 它在小型模型尺寸下实现了高精度,有助于幼儿园管理和激光栽培研究.
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:
- 准确识别苗生长阶段对于优化工厂苗圃管理和确保一致的作物质量至关重要.
- 现有的方法可能缺乏效率或准确性,特别是在资源有限的环境中.
研究的目的:
- 开发和评估PGL-ShuffleNetV2,一种新的轻量级深度学习模型,用于高效和准确地识别苗生长阶段.
- 评估该模型的性能和适合在智能苗木种植系统中部署,包括使用激光补充照明的系统.
主要方法:
- 设计的PGL-ShuffleNetV2具有精简的架构,包括对下采样块的修改和减少单元重复的修改.
- 整合了GELU激活功能,以增强非线性表示和并行加权混合注意模块 (PWMAM),以改进特征提取.
- 在苗数据集上评估了模型的识别准确性和F1分数.
主要成果:
- PGL-ShuffleNetV2实现了98.80%的高识别精度和98.82%的F1得分.
- 该模型拥有0.84 MB的紧尺寸,显示出出色的参数效率.
- 该模型已成功应用于在激光补充照明下分析大米苗,表明其在先进种植研究中的实用性.
结论:
- 在工厂苗圃中,PGL-ShuffleNetV2为苗阶段的识别提供了准确性和效率的最佳平衡.
- 它的轻量级设计使其适合在资源有限的设备上部署,从而促进智能农业监控.
- 该模型为激光技术在智能种植苗种植中的应用提供了宝贵的技术支持.
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