Detection Of Phenotypic Characteristics Of Okra Based On Hyperspectral Imaging Technology

Okra is a common edible crop in China.It has high nutritional value and ornamental value.It has a wide range of varieties and origins in Asia,Europe,North America,Africa and Australia.The environmental resistance of different varieties of okra varies greatly.Therefore,in the process of local breeding and cultivation,it is necessary to use phenotypic information to screen high-yield okra varieties.Nowadays,non-destructive testing technology of crop phenotype has become an urgent need in production and breeding screening.Traditional crop phenotypic parameters acquisition relies on manual measurement,which has high human cost,poor timeliness and the measurement results are vulnerable to subjective judgment.Hyperspectral imaging is a non-destructive,fast and efficient method for plant phenotypic analysis,which has played an increasingly important role in recent years.In the process of plant phenotypic analysis by spectral technology,developing robust algorithms based on different crops has become the key to crop phenotypic measurement and analysis.In addition,the combination of spectral imaging technology to analyze the phenotypic information can effectively reduce the interference of environmental factors,and is the key technology to achieve fine production and breeding screening.In view of the important problems of plant location,phenotypic information acquisition and salt tolerance detection in okra breeding and cultivation,based on spectral imaging,image processing and mathematical modeling technology,this study adopts image segmentation,Hyperspectral Feature wavelength extraction,spectral image information fusion,hierarchical clustering and other theoretical methods to realize the rapid detection of okra phenotypic parameters,and applies phenotypic information and spectral information.The main research results are as follows:?1?Study on physiological and biochemical mechanism of okra under Salt StressIn order to explore the mechanism of salt stress on the phenotype of okra,the changes of biomass,element content and photosynthetic parameters of okra under salt stress were measured and compared with those of the control group without salt stress.The results showed that salt stress could cause root system to absorb large amounts of Na ions,and the K/Na ratio was destroyed.At the same time,salt stress inhibited the photosynthesis of okra leaves.In the early stage of stress,the main reason of photosynthesis rate affected was stomatal closure of leaves,which led to the obstruction of CO₂ supply.After long-term stress,the decline of mesophyll photosynthetic activity resulted in the loss of photosynthetic capacity of okra leaves.?2?Automatic recognition and segmentation of okra based on in-depth learning and prediction of plant biomassIn this study,an image segmentation algorithm model based on deeplearning technology is proposed,which integrates the semantic segmentation module and the instance segmentation module,reduces the model parameters and reduces the computational load.Semantic segmentation module is used to segment the whole plant area,so as to achieve the acquisition of plant canopy area,biomass estimation and visualization of spectral information.Example segmentation module can divide each leaf into individual,providing technical support for the realization of leaf count,leaf area estimation,leaf position estimation and leaf average spectral calculation.Aiming at the problem of high overlap degree of blades,attention mechanism cyclic decoding is introduced to realize blade segmentation.In order to calculate the attention allocation of each blade in decoding process,a candidate probability graph structure is proposed.The blade to be segmented is selected by strategy search cycle,and the calculated attention points are converted into attention coding channel?AAC?embedded in decoding network,so as to obtain more fine segmentation results.At the same time,in order to improve the decoding accuracy,cascade mask prediction is used to predict the blade segmentation mask at each resolution and to calculate the loss function by comparing it with the manual labeling mask.In order to train the segmentation model of okra,732 images of okra plants obtained by hyperspectral imaging were labeled artificially.The data included more than 2000 okra plants.The model is trained on the labeled okra data set.After convergence,the semantics segmentation module achieves 0.94segmentation accuracy?IoU?on the prediction set,which can separate the plant completely from the background,and obviously improves compared with the traditional threshold segmentation method.The symmetrical best dice coefficient?SBD?of the example segmentation module on the prediction set is 81.4,which achieves the independent segmentation of each leaf.There was a high correlation between canopy image area and plant biomass.The canopy image area obtained by image segmentation was used to establish a linear regression model to predict plant biomass.For the prediction model of fresh weight of okra plant,the determination coefficient was 0.6526,and for the prediction model of dry weight of okra plant,the determination coefficient was 0.5997.The results showed that spectral imaging technology could effectively predict plant biomass.?3?SPAD Value Prediction of Okra Based on Visible-Near Infrared Spectral InformationSPAD value is a commonly used indirect evaluation index of chlorophyll content.In this study,the prediction model of SPAD value of okra was established by using visible-near infrared spectroscopy information.Partial least squares regression model is established for the whole band.The K-folding interactive validation shows that when the number of principal components is 12,the effect is the best.At this time,the decision coefficient of training set is 0.7368,the root mean square error is 3.3796,the cross-validation set is 0.7148 and the root mean square error is 3.4838.Because the information of each wavelength in hyperspectral spectrum is highly correlated.Therefore,the feature wavelength extraction algorithm is used to extract a small number of feature wavelengths for modeling,which can effectively alleviate the over-fitting of the model.At the same time,a small number of characteristic wavelengths are more conducive to component analysis and reduce the development cost of spectral detection instruments.In this study,a new feature wavelength search algorithm,adaptive fast Monte Carlo tree search?AFMCTS?,is proposed and compared with the classical feature wavelength extraction algorithm.The algorithm achieves efficient searching in the solution space of characteristic wavelength group,and can quickly approach the optimal solution with a small number of wavelengths.The characteristic wavelengths are extracted by using AFMCTS algorithm and the SPAD value prediction model is established by multiple linear regression.When the characteristic wavelengths are 6,the decision coefficient of training set is 0.7336,the root mean square error is 3.4001,the cross validation set is 0.7217,and the root mean square error is 3.4352.At this time,the results are basically convergent.The eighteen characteristic wavelengths are extracted by CARS algorithm.The partial least squares regression model is used to model the extracted characteristic wavelengths.The determination coefficient of training set is 0.7225,the root mean square error is 3.4703,the determination coefficient of cross validation set is 0.7086,and the root mean square error is 3.5205.The SPA algorithm is used to extract 14 characteristic variables,and the optimal results are obtained.The decision coefficient of training set is 0.7351,the root mean square error is 3.3905,the cross validation set is 0.7182,and the root mean square error is 3.4656.Compared with the classical feature wavelength extraction algorithm and the AFMCTS algorithm,the proposed method has stronger robustness and requires fewer feature wavelengths with the same accuracy.It can get more abundant search results.Therefore,the feature wavelength group extracted by this method is more suitable for practical application.?4?Clustering of salt tolerance level of multi-cultivars of okra based on phenotypic and spectral information.Evaluating crop growth through phenotypic information is of great significance in breeding screening.In order to evaluate the effect of salt stress on the growth of okra,the maximum mean difference?MMD?was introduced by using two-sample detection method.MMD was used to calculate the distribution differences of phenotypic information and physical and chemical information before and after salt stress.The greater the difference was,the more seriously the variety was affected under salt stress.Based on this,the salt tolerance levels of 14 genotypes of okra were calculated.The results showed that the most salt tolerant varieties were`Danzhi',`Xianzhi',`Tokyo Wujiao',`Wufu'.The salt tolerance of the three varieties was relatively poor.The results showed that the salt tolerance level of okra could be effectively evaluated by phenotypic information.However,crop phenotypic information and physical and chemical information have the disadvantage of tedious detection,which is difficult to popularize in practical production.Spectrum has the advantages of fast non-destructive testing,and there is an inherent relationship between spectral information and plant phenotype.Clustering analysis of various varieties of okra by spectral information shows that the clustering results are similar to those based on phenotypic information.Salt tolerance of the strongest'Danzhi'is divided into a separate category.Therefore,it is feasible to evaluate the salt tolerance level of okra using spectral information,which can be used as an effective means to replace phenotypic information.