POMONA: a multiplatform software for modeling seed physiology

Renato Fernandes Cantão; João Paulo Ribeiro-Oliveira; Edvaldo A Amaral da Silva; Amanda Rithieli Dos Santos; Rute Quelvia de Faria; Maria Marcia Pereira Sartori

doi:10.3389/fpls.2023.1151911

POMONA: a multiplatform software for modeling seed physiology

Front Plant Sci. 2023 Jul 6:14:1151911. doi: 10.3389/fpls.2023.1151911. eCollection 2023.

Authors

Renato Fernandes Cantão¹, João Paulo Ribeiro-Oliveira², Edvaldo A Amaral da Silva³, Amanda Rithieli Dos Santos³, Rute Quelvia de Faria³, Maria Marcia Pereira Sartori³

Affiliations

¹ Center for Science and Technology for Sustainability (CSTS), Federal University of São Carlos (UFSCar), Sorocaba, SP, Brazil.
² Instituto de Ciências Agrárias (ICIAG), Universidade Federal de Uberlândia (UFU), Uberlândia, Minas Gerais, Brazil.
³ Department of Crop Science, College of Agricultural Sciences, São Paulo State University (UNESP), Botucatu, SP, Brazil.

Abstract

Seed physiology is related to functional and metabolic traits of the seed-seedling transition. In this sense, modeling the kinetics, uniformity and capacity of a seed sample plays a central role in designing strategies for trade, food, and environmental security. Thus, POMONA is presented as an easy-to-use multiplatform software designed to bring several logistic and linearized models into a single package, allowing for convenient and fast assessment of seed germination and or longevity, even if the data has a non-Normal distribution. POMONA is implemented in JavaScript using the Quasar framework and can run in the Microsoft Windows operating system, GNU/Linux, and Android-powered mobile hardware or on a web server as a service. The capabilities of POMONA are showcased through a series of examples with diaspores of corn and soybean, evidencing its robustness, accuracy, and performance. POMONA can be the first step for the creation of an automatic multiplatform that will benefit laboratory users, including those focused on image analysis.

Keywords: agriculture 5.0; algebraic models; automated scoring; corn; plant physiology; seed quality; soybean.

Grants and funding

This work was supported by the São Paulo Research Foundation (FAPESP) [Grant numbers # 2016/13126-0, 2017/50211-9 and 2018/25698-4 and National Council for Scientific and Technological Development (CNPq) [Grants number 311526/2021-7 and 307024/2021-0].