基于改进的MobileNet V3的Tieguanyin茶的绿化阶段识别
Yuyan Huang1, Shengwei Xia2, Wei Chen2
1Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究介绍了T-GSR,这是一种轻量级的人工智能模型,可自动识别Tieguanyin茶的绿化阶段. 该模型实现了高精度,为茶叶生产的传统感官评估提供了一个客观的替代方案.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 食品科学 食品科学 食品科学
背景情况:
- 绿化阶段对Tieguanyin茶的独特香气和风味有很大的影响.
- 目前用于识别绿化阶段的方法依赖主观感官体验,这是低效和劳动密集的.
研究的目的:
- 开发一种轻量级的自动识别模型 (T-GSR),用于准确和客观地识别Tieguanyin茶的绿化阶段.
- 克服茶叶加工中传统感官评估的局限性.
主要方法:
- 收集了Tieguanyin茶的各种绿化阶段图像的综合数据集.
- 应用图像预处理技术,包括多色空间融合和形态过.
- 通过自适应剩余分支,GELU激活和改进的坐标注意力 (ICA) 机制增强了MobileNet V3骨干.
主要成果:
- T-GSR模型实现了93.38%的识别精度和93.33%的F1得分.
- 与原来的MobileNet V3相比,该模型表现出卓越的性能和降低的复杂性,具有3.025M参数和0.242G FLOPs.
- 拟议的模型有效地捕捉了复杂的茶叶特征,以准确识别阶段.
结论:
- T-GSR模型提供了一个有效的解决方案,用于智能识别Tieguanyin茶的绿化制作阶段.
- 这便于在线监控,并支持茶叶生产过程的自动化.
- 该研究强调了人工智能在增强传统农业实践方面的潜力.
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