Determination of leaf area of sweet pepper based on support vector machine model and image processing

As a vital organ for crop photosynthesis, leaf is one of the major biological indicators in the study on light absorption by crops. Support Vector Machine (SVM) theory was used to set up a SVM model for determination of leaf area of sweet pepper, the input parameters were the leaf length, maximum width of the leaf, and the output parameters were the leaf areas. Data measured by computer image processing technology were trained as samples, the length, maximum width of the leaf were used as input parameters to simulate and test the leaf area. The results were compared with those of linear regression and artificial neural network model. The results showed that the maximum error of leaf area determined by support vector machine model was 6.09%, and the average error was 2.73%, the simulation accuracy was 0.996. This method can well reflect the actual size of leaf area of sweet pepper, and has good practical value and application prospect.