What question did this study set out to answer?

The study aims to develop a high-throughput phenotyping strategy to evaluate flowering-related traits in sugarcane.

April 1, 2026Open Access

High‐throughput phenotyping for the prediction and quantification of flower‐related traits in sugarcane

Key Points

The study aims to develop a high-throughput phenotyping strategy to evaluate flowering-related traits in sugarcane.
Used vegetation indices and artificial intelligence for trait evaluation.
Conducted high-throughput imaging with a DJI drone to capture RGB images.
Processed images to create orthomosaics for analysis of flowering traits.
Applied machine learning and deep learning techniques, including CNN, for data analysis.
CNN model achieved an accuracy of up to 84% in detecting flowering.
Moderate correlation (R² = 0.72) was found between CNN flower counts and field data at the flowering season's onset.
Neural network model reached 0.87 accuracy in differentiating early vs. late-flowering genotypes.
ML regression performed with R² values of 0.51 for flag leaf emergence and 0.52 for days to flowering.

Abstract

Abstract Sugarcane ( Saccharum spp.), a C4 plant, is a vital renewable biofuel and sugar source for industries worldwide. However, synchronizing flowering between parental lines often poses challenges for breeders, hindering effective crossbreeding efforts. This study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods. A total of 154 genotypes were planted in an augmented block design at the IAC sugarcane breeding station in Serra Grande‐BA, Brazil. Raw RGB (Red, Green, Blue) images were captured using a DJI Mavic 3 Enterprise drone during the plant cane (PC) and first ratoon (FR) crop seasons. These images were processed to create orthomosaics and compute metrics/vegetation index; subsequently, machine learning (ML) and deep learning pipelines for systematic analysis were developed. A convolutional neural network (CNN) model achieved promising results, with an accuracy rate of up to 84% in the flowering detection task. Additionally, flower counts from the CNN model showed a moderate correlation with field data, evidenced by an R 2 value of 0.72 at the onset and an R 2 value of 0.29 at the conclusion of the flowering season for the PC. This resulted in an overall average regression R 2 of 0.46 with a root mean square error (RMSE) of 13.80. Furthermore, an artificial neural network classification model reached a notable accuracy of 0.87 in differentiating genotypes based on their flowering response (early‐flowering vs. late‐flowering), utilizing VIs and digital model‐based metrics as input parameters. The ML regression model demonstrated performance levels of R 2 = 0.51 and RMSE = 8.06 for days to flag leaf emergence in PC and R 2 = 0.52 and RMSE = 7.93 for days to flowering in FR. These results highlight the potential of HTP strategies, utilizing orthomosaics and AI, to accelerate data collection and analysis, offering significant insights for breeding programs in sugarcane.

Read Full Paperexternally

Bookmark

View Full Paper

Cite This Study

Santos et al. (Tue,) studied this question.

synapsesocial.com/papers/69cd7aa45652765b073a7ec3 https://doi.org/https://doi.org/10.1002/ppj2.70072

Bookmark

View Full Paper