Image Estimation Method Of Canopy Water Content Of Aquilaria Sinensis

Digital image processing technology is widely used in plant water diagnosis research with its advantages of convenience and efficiency.However,there is currently a lack of research on precious tree species under different stress conditions.In view of this situation,this paper proposes an image method for diagnosing the changes of Aquilaria sinensis leaves under different water stress states,exploring the water conditions suitable for the growth of Aquilaria sinensis,and providing a reference method for realizing the non-destructive monitoring of the sub-deficiency of Aquilaria sinensis..In this paper,the two-year-old precious tree species Aquilaria vulgaris was monitored on soil moisture,and empirical data of water content and nitrogen content under different water stress conditions were obtained.The influence of different degrees of water stress on the ground diameter,crown width and tree height growth of Aquilaria sinensis during the experiment was analyzed.The influence of different sampling heights on the nitrogen content of the leaves of Aquilaria vulgaris,focusing on the construction of a moisture content model based on visible light images and multispectral images,and the use of Cannon EOS750D SLR camera and Mica Sense Red Edge 3 multispectral camera to obtain the Aquilaria vulgaris vertical visible light-near infrared image,use the improved fuzzy local information C-means clustering(FLICM)segmentation algorithm to segment the images of different bands and extract the image features,use the correlation analysis(CA)to eliminate invalid variables,and use the continuous projection algorithm(SPA)Eliminate collinear variables,use Recursive Feature Elimination(RFE)to filter out redundant variables,and try to couple different variable screening methods to screen key variables,combine with Random Forest Algorithm(RF)to build a prediction model of Aquilaria vulgaris leaf moisture content,and finally Using the ten-fold cross-validation method,the continuous projection algorithm＿recursive feature elimination＿random forest fusion model(SPA＿RFE＿RF)is compared with the least square support vector machine(LSSVM)and support vector machine regression(SVR)models to test the feasibility of the model,and Discuss the effects of different stresses on the nitrogen content indexes of Aquilaria leaves.The main conclusions obtained are as follows:(1)During the experiment period,soil moisture in the 80%-90%treatment group(T3treatment group)had little effect on the growth of ground diameter,but had extremely significant effect on the growth of plant height and canopy width.Compared with the T3treatment group,the growth of plant height was significantly decreased at the soil water content of 30%-40%(T1 treatment group),indicating that drought would increase the negative effect on plant height in the short term.In addition,both drought and waterlogging significantly reduced leaf nitrogen content,and appropriately increasing water content was beneficial to improve nitrogen utilization efficiency of agallowood.Under extreme drought conditions,drought became the dominant factor affecting nitrogen content of Aquilaria vulgaris,and in this study,drought had no significant effect on the aboveground and underground distribution of nitrogen content.(2)Compared to T3 waterlogging treatment group,the Aquilaria sinensis for T1 drought treatment group is more sensitive,and although there are certain self-adjustment ability Aquilaria sinensis,but drought longer than two weeks can make seedling leaf was badly damaged,threat aloes normal growth,suggests that Aquilaria vulgaris seedlings under continuous drought in two weeks,can remove harmful substances in the cell by adjusting themselves,However,when the drought lasted for more than two weeks,the normal growth of Aquilaria vulgaris was threatened.In addition,the research showed that the optimal water growth range of Aquilaria vulgaris leaves was 50%-65%,and a moderate increase in water content was beneficial to the growth of aloes.(3)The FLICM algorithm can effectively solve the traditional fuzzy clustering algorithm’s sensitivity to noise and outliers,realize the effective segmentation of multi-band blurred images,and provide a reference for the accurate segmentation of information-rich multi-band blurred images;the model adds R,G,The gray value of B,RE,and NIR bands are used as input parameters,which overcomes the shortcomings of a single band that is easily affected by the environment,integrates more band information on the input parameters,and improves the accuracy of the model;(4)The sensitivity,false positive rate and accuracy of the water content prediction model based on SPA＿RFE＿RF fusion algorithm were 0.976,0.853,0.144 and 0.956.The training set R~2,MAPE,RMSE and MSE of the multi-spectral image water content prediction model based on the SPA＿RFE＿RF fusion algorithm were 0.964,20.67,0.199 and 0.039.The test set R~2 was0.945,MAPE was 0.016,RMSE was 2.304,and MSE was 5.308.Compared with SPA＿RFE＿LSSVM and SPA＿RFE＿SVR,the accuracy of the proposed model is 12.459%and6.493%higher than that of SPA＿RFE＿SVR.