GAE-YOLO:一个轻量级的多模式检测框架,用于番茄智能农业与边缘计算
Xiaoke Liu1,2, Wenjie Teng1, Haoran Yu3
1School of Basic Medical Sciences, Shandong Second Medical University, Weifang, Shandong, China.
Frontiers in plant science
|December 5, 2025
概括
一个新的智能番茄管理系统使用GAE-YOLO算法进行高效的作物监测. 这种智能农业解决方案增强了番茄种植中的实时能力和多任务协调.
科学领域:
- 计算机视觉 计算机视觉
- 智能农业 智能农业
- 边缘计算 边缘计算
背景情况:
- 计算机视觉在智能农业中越来越多地用于作物监测.
- 现有的方法在计算复杂性,实时处理和多任务协调方面面临挑战.
研究的目的:
- 开发一种智能番茄管理系统,解决当前计算机视觉方法的局限性.
- 改进番茄种植中的实时能力,计算效率和多任务协调.
主要方法:
- 提出了一个基于幽灵的自适应高效的你只看一次 (GAE-YOLO) 算法.
- 使用了幽灵卷积 (GhostConv),AReLU激活和有效交叉在联盟 (E-IoU) 损失上.
- 在Jetson TX2平台上实现,具有ZED立体视觉和PyQt6可视化接口.
主要成果:
- 在Jetson TX2.2.上以10.2 FPS实现了93.5%的平均平均精度 (mAP@50).
- 使用TensorRT加速和720p分辨率优化至27FPS.
- 建立了番茄成熟度和产量预测,疾病诊断和LLM咨询的系统.
结论:
- GAE-YOLO系统为农业边缘计算提供了一个新的范式.
- 为推进智能农业提供关键的技术支持.
- 在实时番茄管理和分析中表现出增强的性能.
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