Model generalisation for the detection of tea moisture content was investigated across different leaf surface orientations and tea varieties in this study. The micromorphology of leaves plucked from three tea bushes was analysed, and differences between different surface orientations and varieties were observed. The VNIR spectra (350–2500 nm) of the leaves were collected and analysed. Excellent prediction performance was obtained for moisture detection models based on spectra for the same leaf surface orientation and variety. By contrast, the prediction performance decreased severely if the test spectra were obtained for different leaf surface orientations and varieties. To solve this issue, differential treatments with fractional order between 0 and 2 were carried out on the spectra. The results showed that the prediction performance improved for generalisation between varieties and orientations, especially for orders of 0.4 or 0.6. The mechanism by which the fractional differential treatment mines the common information from the spectra with varying characteristics was elucidated by calculating the correlation coefficients between the moisture content and the spectra treated with different differential orders. The results of this study advance tea moisture detection based on VNIR spectra and the ability to generalise across spectra with different characteristics.

在本研究中，研究了跨不同叶表面方向和茶品种的茶水分含量检测模型的一般化方法。分析了从三个茶树上摘下的叶子的微观形态，观察了不同表面取向和品种之间的差异。收集并分析了叶片的VNIR光谱（350-2500 nm）。基于相同叶片表面方向和品种的光谱，水分检测模型获得了出色的预测性能。相反，如果获得了针对不同叶片表面方向和品种的测试光谱，则预测性能将大大降低。为了解决这个问题，在光谱上进行了分数阶在0和2之间的微分处理。结果表明，对于品种和方向之间的泛化，预测性能有所提高，尤其是对于0.4或0.6的量级。通过计算水分含量与不同差分阶数处理的光谱之间的相关系数，阐明了分数差分处理从具有变化特征的光谱中挖掘公共信息的机制。这项研究的结果促进了基于VNIR光谱的茶叶水分检测以及对具有不同特征的光谱进行泛化的能力。通过计算水分含量与不同差分阶数处理的光谱之间的相关系数，阐明了分数差分处理从具有变化特征的光谱中挖掘公共信息的机制。这项研究的结果促进了基于VNIR光谱的茶叶水分检测以及对具有不同特征的光谱进行泛化的能力。通过计算水分含量与不同差分阶数处理的光谱之间的相关系数，阐明了分数差分处理从具有变化特征的光谱中挖掘公共信息的机制。这项研究的结果促进了基于VNIR光谱的茶叶水分检测以及对具有不同特征的光谱进行泛化的能力。