The methodology allows for faster, more cost-effective and comprehensive data collection
Brazilian researchers are applying an innovative methodology that accelerates the selection of genetically modified corn plants 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 more quickly identify the most promising specimens and simulate their performance in different climate conditions, making the selection process more efficient and accurate.
The study was conducted by researchers from the Center for Applied Genomics for Climate Change (GCCRC), a partnership between Embrapa and the State University of Campinas (Unicamp), with support from the São Paulo Research Foundation (FAPESP). The results were published in the journal The Plant Phenome Journal
The increased frequency and severity of droughts due to climate change makes it essential to develop more resilient cultivars. However, traditional field evaluation 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 perform manual measurements, often with expensive equipment and slow processes,” explains 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 to flowering and plant height. Now, we can do it in just a few hours, with drone flights and image processing,” highlights Yassitepe.
Field experiments
During the 2023 dry season, two experiments were conducted in Campinas (SP), over a period of five months. Twenty-one varieties of corn were cultivated, 21 with genes that were being tested for drought tolerance and 18 without genetic alterations, for comparison. The plants were subjected to controlled management conditions, with the difference being in one variable: irrigation. “One group received water throughout the cycle, while the other was subjected to drought,” explains Yassitepe.
The drones conducted weekly flights 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.
Cost reduction and greater efficiency
In addition to reducing operating costs, the methodology allows studies to be conducted 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,” explains Yassitepe. “With lower flights, it is possible to obtain high-resolution images, allowing more corn varieties to be tested in the same area,” adds Helcio Pereira, a postdoctoral researcher at GCCRC and co-author of the study.
This approach also makes it possible to monitor plant development throughout the entire growth cycle. “Continuous temporal analysis was essential to understand how plants respond to water stress,” explains Pereira.
The detailed data collected by the drones was 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,” says Pereira.
The study “Temporal field phenomics of transgenic maize events subjected to drought stress: cross-validation scenarios and machine learning models ”, written by Helcio Pereira, Juliana Nonato, Rafaela Duarte, Isabel Gerhardt, Ricardo Dante, Paulo Arruda and Juliana Yassitepe, can be accessed at the link below.
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