通过深度学习提升SIS-YOLOv8用于Solanaceae作物监测,推进精准农业
Ruiqian Qin1, Yiming Wang2, Xiaoyan Liu1,3
1College of Information Technology, Jilin Agricultural University, Changchun, China.
Frontiers in plant science
|January 24, 2025
概括
一个新的SIS-YOLOv8模型改善了在具有挑战性的气候条件下发现土豆和番茄病的方法. 这种人工智能方法提高了可持续农业的准确性和稳定性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的作物疾病监测依赖于低效的手工检查,容易产生主观偏见.
- 像YOLO这样的深度学习模型显示出希望,但与复杂的农业环境和跨物种疾病识别斗争.
研究的目的:
- 开发一个增强的YOLOv8模型 (SIS-YOLOv8) 以在各种气候条件下对土豆和西红进行强大而准确的疾病检测.
- 提高人工智能模型用于农业疾病监测的适应性和泛化能力.
主要方法:
- 提出了SIS-YOLOv8模型与Fusion-Inception Conv模块,用于在不利条件下 (雨,雾) 改进特征提取.
- 整合了一个C2f-SIS模块与风格随机化,用于在不同作物疾病中进行增强的概括.
- 实现了一个SPPF-IS模块用于特征融合,以提高模型的稳定性.
- 利用Dep Graph修剪方法将模型参数大小减少了19.9%和计算负载.
主要成果:
- 与原始YOLOv8n模型相比,SIS-YOLOv8模型在检测土豆和西红中的疾病方面表现优越.
- 在精度上提高了8.2%,在回忆率上提高了4%,在mAP50上提高了5.9%,在mAP50-95.5上提高了6.3%.
结论:
- 该SIS-YOLOv8模型为复杂的农业环境提供了更好的适应性,为自动检测作物疾病提供了有效的解决方案.
- 优化模型的效率和稳定性有助于推进农业疾病监测和促进人工智能驱动的可持续作物管理.
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