Research On Tomato Growth Detection Method Based On Terahertz Fusion 3D Light Field Multi Source Imaging And Its Application

Tomato is one of the most widely planted protected fruits and vegetables in China,and it is an important part of the vegetable basket project.Reasonable planting and management of Tomato in facility environment has always been the focus.Using information technology to obtain the growth information of tomato can not only provide the basis for water and fertilizer management and regulation,but also improve the quality of tomato,and is conducive to the green and sustainable development of facility agriculture.Therefore,in order to fully obtain the growth information of tomato,this paper uses 3D light field imaging,terahertz imaging and information fusion technology to carry out the research on the detection method and application of tomato growth.The main research contents are as follows:(1)The experimental data of different scales were obtained.In order to make the sample data closer to reality,we used perlite as the matrix,and used 3D light field imaging system to collect the external phenotypic information of tomato plant height,stem diameter,crown area,leaf area and crop volume under different potassium concentrations;Terahertz time domain spectral imaging system was used to obtain the terahertz spectral tomography image information of tomato leaves in the range of 0-4.0 THz;At the same time,traditional classical methods are used to collect data.(2)The tomato information of different scales obtained by 3D light field imaging system and terahertz imaging system were processed.For the characteristic parameters of stem diameter,leaf area and crown area calculated by the point cloud data,the bilateral filtering algorithm is used to denoise the point cloud data,and then the space algorithm is used to sparse the point cloud.For the characteristic parameters of stem diameter,the maximum and minimum values of the horizontal point cloud are found,and then the difference is calculated;For the characteristic parameters of leaf area and crown area,the delaurot triangulation algorithm is used to transform the point cloud from independent points into a surface graph which can be used to calculate leaf area,and then the programming calculation is carried out by using Python and VTK library;According to the characteristic parameters of plant volume,firstly,the point cloud data of the plant is spliced,and then the convex hull algorithm is used to calculate the volume;According to the characteristic parameters of plant height,firstly,the depth map colors at different heights were obtained by height calibration using standard plate,then the depth map of tomato samples at different heights was obtained,and the sample height was calculated by comparing the highest point of the sample with the depth map of standard plate;Finally,a multiple regression model was established between the growth characteristics of tomato and the nutrient content of crop.Based on the obtained terahertz spectral image data,the moving average algorithm,iterative adaptive weighted penalty least square algorithm and multiple scattering correction algorithm are used to preprocess the data,and then the principal component analysis is used to reduce the dimension of the data.The first three principal components representing most of the information of the data are found for image analysis and the multiple regression model is established.(3)Considering the limitation of single detection method of crop nutrition nondestructive testing,the data measured by two detection devices are substituted into multiple regression algorithm and BP neural network algorithm,and then the correlation coefficients under different model algorithms are obtained respectively.The correlation coefficients under different concentrations in different periods obtained by multiple regression algorithm are between 0.903 and 0.931,The correlation coefficients obtained by BP neural network are between 0.917 and 0.940,while when using the same data for calculation,the correlation coefficients obtained by BP neural network are better than the multivariate regression algorithm.(4)Due to the high price of the above experimental equipment,a portable detection platform suitable for greenhouse with a variety of nutrition and growth detection equipment was built,and the main functions and components of the platform were introduced.