Using drones accelerates genetic improvement of corn plants in search of drought tolerance

Date:01-08-2025

Brazilian researchers are applying an innovative methodology that accelerates the selection of corn plants genetically modified to resist drought and reduces operational costs involved in the task. 

The technique uses drones equipped with RGB cameras to capture images of field experiments, converting them into indexes that assess the health of the plants. With this information, it is possible to quickly identify the most promising specimens and simulate their performance in different climate conditions, making the selection process more efficient and accurate.

Researchers from the Center for Genomics Applied to Climate Change (GCCRC), conducted the study in a partnership between Embrapa and the State University of Campinas (Unicamp), with support from the São Paulo Research Foundation (Fapesp). The results were published yesterday, January 5, in The Plant Phenome Journal.

Climate change is increasing the frequency and severity of droughts, making it essential to develop more resilient cultivars. However, traditional field assessment methods are time-consuming and expensive, making rapid progress difficult. 

″With conventional methods, it is necessary to wait for the plant to complete its cycle and take manual measurements, often with expensive equipment and slow processes,″ explained Juliana Yassitepe, a researcher at Embrapa Digital Agriculture and author of the study.

With the new methodology, data collection in the field has been significantly optimized. ″Before, it would take several days to measure grain production, time until flowering, and plant height. Now, we can do this in a few hours, with drone flights and image processing,″ Yassitepe highlighted.

Field experiments

During the 2023 dry season, two experiments were carried out in Campinas (SP), over the course of five months. Twenty-one varieties of corn were grown, 18 with genes that were being tested for drought tolerance and three without genetic alterations, for comparison. The plants were subjected to controlled management conditions, with the difference being in a single variable: irrigation. ″One group received water throughout the cycle, while the other was subjected to drought,″ Yassitepe explained.

The drones flew weekly over the experimental field, capturing images with RGB (conventional) and multispectral cameras (which capture non-visible spectra, such as infrared). The analysis revealed that RGB cameras, which are significantly cheaper than multispectral cameras, produce reliable results, making the technology accessible for large-scale genetic improvement programs.

Reduced costs and greater efficiency

In addition to reducing operating costs, the methodology allows studies to be carried out in smaller areas, which is especially useful in projects with limited resources. ″This issue of planted area is sometimes a bottleneck in plant genetic improvement studies since the research group does not always have many viable seeds to plant in very large areas,″ Yassitepe explained. 

″With lower flights, it is possible to obtain high-resolution images, allowing more corn varieties to be tested in the same area,″ added Helcio Pereira, a postdoctoral researcher at GCCRC and co-author of the study.

This approach also makes it possible to monitor the development of plants throughout the entire growth cycle. ″Continuous temporal analysis was essential to understand how plants respond to water stress,″ Pereira explained.

The detailed data collected by the drones were used to develop predictive models that help select corn varieties adapted to different environmental conditions. ″With these models, we can predict the behavior of plant varieties without the need for frequent manual measurements, making the process faster and more accessible,″ Pereira said.

The study ″Temporal field phenomics of transgenic maize events subjected to drought stress: cross-validation scenarios and machine learning models,″ authored by Helcio Pereira, Juliana Nonato, Rafaela Duarte, Isabel Gerhardt, Ricardo Dante, Paulo Arruda, and Juliana Yassitepe, can be accessed here.