Dynamic growth tomato inflorescence modeling with elastic mechanics data

• Tomato inflorescence modeling method with elastic coefficient. • Relation between tomato inflorescence size and elastic coefficient. • Dynamic growth model of tomato inflorescence flowering and fruiting. • Tomato inflorescence modeling software developed with OpenGL. Current crop modeling methods can not simulate crop dynamic growth containing physical characteristics data, while the study of physical production management such as pollination of tomato requires the elastic force and dynamic growth process analysis of the inflorescence. Therefore, this study proposes a tomato inflorescence modeling method to meet the modeling demands of combining the growth process and elastic mechanics data. With the model promoted in this study, after measuring the size of tomato inflorescence key structures, the growth progress of inflorescence can be predicted, and elastic mechanics data of inflorescence at various sizes can be obtained. Firstly, based on plant topology theory, tomato inflorescence was divided into structural skeleton and tomato flower (or fruit). Where the structural skeleton was divided into four parts, that are peduncle, primary flower stalk, secondary flower stalk, and pedicel. Then, the diameters, lengths, growth coefficients and elastic coefficients of tomato inflorescence key structures were measured at different growth stages, the principle between key structures size and the elastic coefficient was established. Finally, a software interface is designed based on the MFC framework with the OpenGL library, which can generate dynamically growing inflorescence model, which contain the elastic mechanics data of inflorescence model. The experimental results show that the average prediction error of inflorescence size in the established model is 8.35 %, and the average estimation error of elasticity coefficient is 7.41 %. The study result lays the foundation for the establishment of tomato inflorescence modeling method, which can help to achieve the study of tomato physical production management. The modeling method proposed in this study also provides new ideas and methods for plant modeling that simultaneously simulate crop dynamic growth and contain physical characteristics data.