TRD-Net:基于改进的YOLO v8进行选择性收获的高效番茄成熟度检测网络
Xiangpeng Fan1,2, Xiujuan Chai1,2
1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.
Frontiers in plant science
|February 13, 2026
概括
一个新的轻量级番茄成熟度检测网络 (TRD-Net) 在复杂的环境中改善了实时识别和成熟度检测. 这个模型为选择性收获机器人提供更快的速度和更低的计算需求.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 选择性收获需要精确的果实识别和成熟度检测.
- 现有的方法在复杂的环境中面临挑战,包括遮蔽和照明变化.
研究的目的:
- 开发一种新的,轻量级的番茄成熟度检测网络 (TRD-Net),以在现实条件下提高性能.
- 为了增强自动收获系统的实时番茄识别和成熟度检测.
主要方法:
- 创建了一个由3330个现实世界的图像组成的西红数据集.
- 使用空间和通道重建卷积 (SCRConv) 和SimAM注意力,改善了YOLO v8s网络.
- MPDIoU损失函数取代了CIoU损失,用于增强检测.
主要成果:
- TRD-Net实现了0.9581的mAP@0.5,改善了4.32%.
- 模型大小减少了19.69%,推断时间为每张图像8.7毫秒.
- 参数和FLOP分别减少了19.69%和22.03%.
结论:
- TRD-Net在番茄识别和成熟度检测准确性和效率方面取得了显著的改进.
- 该模型非常适合复杂园艺环境中的实时应用.
- 这项研究支持为选择性收获机器人开发机器视觉系统.
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