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Mueller matrix microscopy is capable of polarization characterization of pathological samples and polarization imaging based digital pathology. In recent years, hospitals are replacing glass coverslips with plastic coverslips for automatic preparations of dry and clean pathological slides with less slide-sticking and air bubbles. However, plastic coverslips are usually birefringent and introduce polarization artifacts in Mueller matrix imaging. In this study, a spatial frequency based calibration method (SFCM) is used to remove such polarization artifacts. The polarization information of the plastic coverslips and the pathological tissues are separated by the spatial frequency analysis, then the Mueller matrix images of pathological tissues are restored by matrix inversions. By cutting two adjacent lung cancer tissue slides, we prepare paired samples of very similar pathological structures but one with a glass coverslip and the other with a plastic coverslip. Comparisons between Mueller matrix images of the paired samples show that SFCM can effectively remove the artifacts due to plastic coverslip.

The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential. Based on partial least square (PLSR), multiple linear regression (MLR), support vector machine (SVM), random forest (RF), BP neural network and other machine learning algorithms, the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum, first-order differential spectrum, combined spectrum index and vegetation index (VI) and their coupled combination variables. The accuracy of the models is compared and analyzed, and the best modeling method of biomass in different growth stages is selected. Based on the optimized modeling method, the biomass of each growth stage is estimated, and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method, and the accuracy of the model is verified. The results showed that in tuber formation stage, starch accumulation stage and maturity stage, the biomass estimation accuracy based on combination variable was the highest, the best modeling method was MLR and SVM, in tuber growth stage, the best modeling method was MLR, the effect of yield estimation is good. It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

Leaf Area Index (LAI) is an important parameter for assessing the crop growth and winter wheat yield prediction. The objectives of this study were(1)to establish and verify a model for the LAI of winter wheat, where the regression models, extended the Grey Relational Analysis(GRA), Akaike's Information Criterion(AIC), Least Squares Support Vector Machine (LSSVM) and (ii) to compare the performance of proposed models GRA-LSSVM-AIC. Spectral reflectance of leaves and concurrent LAI parameters of samples were acquired in Tongzhou and Shunyi districts, Beijing city, China, during 2008/2009 and 2009/2010 winter wheat growth seasons. In the combined model, GRA was used to analyse the correlation between vegetation index and LAI, LSSVM was used to conduct regression analysis according to the GRA for different vegetation index order of the number of independent variables, AIC was used to select the optimal models in LSSVM models. Our results indicated that GRA-LSSVM-AIC optimal models came out robust LAI evaluation (R2 = 0.81 and 0.80, RMSE =0.765 and 0.733, i ndividually). The GRA-LSSVM-AIC had higher applicability between different years and achieved prediction of LAI estimation of winter wheat between regional and annual levels, and had a wide range of potential applications.