A Novel Tomato Volume Measurement Method based on Machine Vision

Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%