To improve the quality of green tea, low light stress has been used to increase the chlorophyll-a (chl-a) content of tea leaves, although shading treatments sometimes lead to early mortality of tea trees. Therefore, in situ measurement of chl-a and chlorophyll-b (chl-b), which are markers for evaluating light stress and response to changing environmental conditions, can be used to improve tea tree management. Chlorophyll content estimation is one of the most common applications of hyperspectral remote sensing, but most prior studies have evaluated samples grown under relatively low stress. Therefore, the results of prior studies are not applicable for estimating chl-a and chl-b contents of shade-grown tea. Machine learning algorithms have recently attracted attention as an approach for evaluating biochemical properties. In the present study, three different common machine learning algorithms were compared, including random forests, support vector machines and deep belief nets. The ratios of performance to deviation (RPDs) of deep belief nets (DBN) were always larger than 1.4 (the ranges of RPD were 1.49–4.92 and 1.48–5.10 for chl-a and chl-b, respectively), suggesting that DBN is a unique algorithm that can reliably be used for estimation of chl-a and chl-b contents.

为了改善绿茶的质量，虽然遮光处理有时会导致茶树的早期死亡，但低光照胁迫已被用于增加茶叶的叶绿素a（chl a）含量。因此，在chl-的原位测量一个和叶绿素b（chl- b），这是用于评价光应力和响应于变化的环境条件的标记，可用于改善茶树管理。叶绿素含量估算是高光谱遥感最普遍的应用之一，但是大多数先前的研究已经评估了在相对较低压力下生长的样品。因此，以前的研究的结果是不适用的用于估计chl-一个和chl-b阴凉茶的内容物。机器学习算法作为一种评估生化特性的方法最近已引起人们的关注。在本研究中，比较了三种不同的通用机器学习算法，包括随机森林，支持向量机和深度置信网。深度信任网（DBN）的性能与偏差（RPD）的比率始终大于1.4（对于chl- a和chl- b，RPD的范围分别为1.49–4.92和1.48–5.10 ），这表明DBN为可以可靠地用于估计chl- a和chl- b含量的独特算法。