Estimation of photosynthetic indexes in strawberry leaves based on RGB model
In order to explore the feasibility of using RGB image feature and SPAD value in photosynthetic indexes prediction,strawberry leaves were selected as experimental materials in this study.Multiple linear regression model and back propagation(BP)neural network model were constructed to estimate leaf transpiration rate,stomatal conductance,net photosynthetic rate and intercellular CO2 concentration,and their accuracy was evaluated and verified.The results showed that the prediction of leaf transpiration rate by using RGB color parameters and SPAD values based on BP neural network model was better,followed by stomatal conductance.The estimation accuracy of BP neural network model was high-er than that of multiple linear regression model,and the prediction accuracy of transpiration rate,stomatal conductance,net photosynthetic rate and intercellular CO2 concentration reached 91.5%,83.3%,74.4%and 71.5%,respectively.The de-termination coefficients(R2)of transpiration rate model and stomatal conductance model based on BP neural network were 0.922 2 and 0.842 3,the root mean square errors(RMSE)were 0.000 2 and 0.025 9,and the mean absolute errors(MAE)were 0.000 1 and 0.000 6,respec-tively.Therefore,the transpiration rate and stomatal con-ductance of strawberry leaves can be easily and quickly es-timated by using digital camera to collect images and con-struct RGB model,which can be used to predict photosynthetic indexes of strawberry in production.
strawberry leavesRGB modelphotosynthetic indexback propagation(BP)neural network model
万方数据
维普
