Automation of floral sex determination in sugarcane using the pliman package and comparison with visual evaluation

Authors

  • Emanuel Nascimento UFAL
  • João Messias dos Santos Campus de Engenharias e Ciências Agrárias, Universidade Federal de Alagoas
  • Paulo Pedro da Silva Campus de Engenharias e Ciências Agrárias, Universidade Federal de Alagoas
  • Carlos Assis Diniz Campus de Engenharias e Ciências Agrárias, Universidade Federal de Alagoas
  • Wedja Barbosa Ferreira Campus de Engenharias e Ciências Agrárias, Universidade Federal de Alagoas
  • Daniel Freitas Soares Campus de Engenharias e Ciências Agrárias, Universidade Federal de Alagoas

Keywords:

Image analysis, Floral sexing, Pollen viability, Sugarcane breeding

Abstract

The determination of floral sex in sugarcane is essential for planning crosses in breeding programs. Traditional visual assessment methods, although common, are subjective and time-consuming. This study evaluated the use of the pliman package in R software for automated classification of pollen images, comparing it with visual evaluation in 10 parents at the Serra do Ouro Flowering and Crossing Station in 2025. Both methods showed a high correlation (r=0.84) and 70% agreement, indicating that pliman can replace or complement visual assessment. Automation reduced analysis time and standardized results, reinforcing its potential as a support tool for sugarcane breeding.

References

Amaral, A. L., Santos, J. M., Camara, T. M. M., & Barbosa, G. V. S. (2012). Metodologia de conservação de pólen de cana-de-açúcar. Aracaju: Embrapa Tabuleiros Costeiros.

Ascari, L., Novara, C., Dusio, V., Oddi, L., & Siniscalco, C. (2020). Quantitative methods in microscopy to assess pollen viability in different plant taxa. Plant Reproduction, 33(2), 205–219.

Ayenan, M. A. T., Danquah, A., Ampomah-Dwamena, C., Hanson, P., & Asante, I. K. (2020). Optimizing PollenCounter for high throughput phenotyping of pollen quality in tomatoes. MethodsX, 7, 100977.

Cursi, D. E., Benício, M. S., Silva, J. F., Balsalobre, T. W. A., Santos, J. M., & Barbosa, G. V. S. (2022). History and current status of sugarcane breeding, germplasm development, and molecular genetics in Brazil. Sugar Tech, 24(1), 112–133.

Gómez, F. E. (1962). Estudio de la fertilidad en caña de azúcar. Agronomía Tropical, 12(2), 143–156.

Machado Jr., J. (1987). Sexualidade e reprodução em cana-de-açúcar. Recife: Universidade Federal de Pernambuco.

Olivoto, T. (2022). Lights, camera, pliman! An R package for plant image analysis. Methods in Ecology and Evolution, 13(4), 789–798.

Padilha, K. S. M. A., Silva, M. A., Torres, R. C., & Costa, A. M. (2024). Use of image analysis in the evaluation of radicular nodules in chickpeas. Caderno Pedagógico, 21, e7318.

R Core Team. (2020). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

Zhang, C. J., Liu, H., Yang, R., & Wu, S. (2024). DeepPollenCount: a swin-transformer-YOLOv5-based deep learning method for pollen counting in various plant species. Aerobiologia, 40(1), 1–12.

Downloads

Published

2026-08-03

Issue

Vol. 1 No. 1 (2025): Proceedings of the III Workshop on Data Analysis

Section

Abstract (Resumo Expandido WANDA 2025)

License

Copyright (c) 2026 Emanuel Nascimento, João Messias dos Santos, Paulo Pedro da Silva , Carlos Assis Diniz , Wedja Barbosa Ferreira, Daniel Freitas Soares

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish in the Annals of Data Analysis retain

the copyright of their work and agree to license it under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.