使用YOLOv8-SPP进行生物废物管理的先进预测分析,以提高智能城市的废物预测和可持续性
Selvalakshmi Balasubramanium1, Bharathiraja Nagu2, Shonak Bansal3
1Department of Computer Science and Engineering, Tagore Engineering College, Chennai, 600127, Tamil Nadu, India.
Scientific reports
|July 3, 2025
概括
这项研究引入了生物废物管理的新预测分析模型,在废物生产预测中达到92%的准确性. 这种人工智能驱动的方法可以提高20%的回收率,并将处理成本降低15%.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 大量的生物废物对健康和环境卫生构成重大风险.
- 有效的生物废物管理对于将废物转化为低碳能源至关重要.
- 目前的废物管理策略需要优化,以提高效率和可持续性.
研究的目的:
- 引入一种新的预测分析模型,以加强生物废物管理.
- 提高废物生产预测和趋势预测的准确性.
- 证明先进分析对废物管理效率和资源利用的影响.
主要方法:
- 使用YOLOv8-SPP算法的预测分析模型的开发.
- 精确的结构和数据处理用于废物识别和细分.
- 对YOLOv8-SPP模型与预测准确性的其他数据类型进行比较分析.
主要成果:
- YOLOv8-SPP模型在预测废物产生方面达到92%的准确性,明显优于其他数据类型 (78%的准确性).
- 该框架的部署导致回收率增加了20%.
- 在实施新模式后,废物处理费用减少了15%.
结论:
- 最先进的分析,特别是YOLOv8-SPP模型,可以显著优化废物管理流程.
- 拟议的模型为智能城市提供了一个可行的解决方案,以有效地应对生物废物挑战.
- 这项研究证实了人工智能在改善废物管理,促进循环经济和提高城市可持续性方面所带来的好处.
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