College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China.
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling, Shaanxi, 712100, China.
Sci Rep. 2020 Feb 20;10(1):3013. doi: 10.1038/s41598-020-59741-6.
Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.
由于冠层新生叶片光合器官发育不完善，光合能力不足，其光合作用强度不仅与外界环境因素有关，而且与叶片内部机制特性显著相关。当外部光照过强时，很可能会发生光抑制甚至光破坏。因此，关注冠层新生叶片，准确构建基于环境因子分析和荧光机制特征分析的光合速率预测模型已成为设施农业需要解决的关键问题。针对上述问题，提出了一种黄瓜冠层新生叶片光合速率预测模型。通过多因素试验设计，获得了大量的冠层新生叶片光合荧光多切片大数据。采用相关分析方法，获取主要环境影响因素作为模型输入，并利用核心叶绿素荧光参数进行辅助验证。采用最优建模方法 PSO-BP 神经网络构建了新生叶片光合速率预测模型。验证结果表明，该模型可以准确预测黄瓜冠层叶片在不同环境因素下的净光合速率。光合速率实测值与预测值之间的决定系数为 0.9947，均方根误差为 0.8787。同时，结合核心荧光参数进行辅助验证，发现荧光参数可以准确地描述作物的光合作用。因此，本研究对于提高设施作物新叶光环境调控精度具有重要意义。
