基于深度学习的玉米种子表型预测电磁振动参数的多目标优化.
Xinwei Zhang1, Zeen Wang2, Kechuan Yi2
1College of Mechanical Engineering, Anhui Science and Technology University, Chuzhou, 233100, Anhui, China. xwzhang1983@163.com.
Scientific reports
|October 22, 2025
概括
本研究介绍了一种适应性框架,用于优化使用深度学习的电磁玉米种子处理. 这种新的方法显著提高了发芽和活力,推进了精准农业.
科学领域:
- 农业工程 农业工程
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 传统的玉米种子处理方法缺乏精度和适应性.
- 优化电磁参数是复杂的,因为多变量相互作用.
- 深度学习为农业过程中的智能控制提供了潜力.
研究的目的:
- 开发一种用于在玉米种子处理中适应优化电磁振动参数的新框架.
- 利用多目标深度学习来预测种子表型特征和优化治疗方案.
- 提高种子质量指标,如发芽率和活力指数.
主要方法:
- 开发了一种混合卷积神经网络-长期短期记忆 (CNN-LSTM) 网络,用于处理传感器数据.
- 集成的遗传算法和粒子群优化,用于实时参数调整.
- 验证了三种玉米品种 (丹958种,洋335种,开968种) 的框架.
主要成果:
- 优化治疗方案导致发芽率增加12.8%,生力指数改善17.7%.
- 多目标深度学习模型实现了93.7%的预测准确度和91.2%的回忆.
- 适应性策略成功地平衡了治疗效率,能源效率和处理时间.
结论:
- 新的框架为智能种子处理系统提供了全面的解决方案.
- 这项研究显著推进了精准农业和可持续作物生产技术.
- 适应性优化策略在不同种子批次中表现出强的性能.
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