Research published in Nature Scientific Reports has highlighted the huge potential of very high resolution space data to improve the detection of a plant disease that impacts a major global cereal crop.

The authors hope the study will be the first step to developing a satellite-enabled early warning system for crop health that operates over regional and even national scales.

Led by the International Maize and Wheat Improvement Center (CIMMYT) at its site in Ethiopia, the research drew on imagery from the SkySat and Pléiades constellations, which were delivered via ESA’s Third Party Missions programme. These data were used alongside observations from uncrewed drones.

The project – completed in partnership with the Ethiopian Institute of Agricultural Research (EIAR) and New Zealand-based Lincoln Agriotech Ltd – focused on a group of fungal crop diseases called wheat rusts, which can destroy entire yields within a matter of weeks.

Consequently, wheat rusts are a major threat to agricultural production, particularly in Asia and Africa, where up to 25% of the world’s wheat is grown.

To help address this challenge, the research team built on past research that drew on remote sensing data to support detection and control strategies.

Gerald Blasch, Earth observation specialist at CIMMYT-Ethiopia and lead author of the study, explained: “Satellite data have been used to detect wheat rusts in previous study, but these investigations have tended to focus on crops in the later stages of growth, when the effects are clearly visible – unfortunately, by this point, it may be too late for control measures to be effective in reducing losses.

“In the current research, we aimed to detect more subtle signs of disease that manifest in earlier growth stages, giving farmers more time to apply fungicides and protect their crops. Very high resolution data from ESA’s Third Party Missions programme were vital in enabling us to achieve this objective.”

At EIAR’s Kulumsa Agricultural Research Center, Blasch and his team completed a field trial in which several varieties of wheat – with varying levels of resistance and susceptibility – were infected with yellow and stem rusts, and exposed to treated and non-treated fungicide regimes.

Disease development was assessed manually by plant pathologists at planned time points using visual estimations. These observations were compared with disease scores derived from data delivered by SkySat and Pléiades, as well as the uncrewed drone system, which overflew the study site at regular intervals.

SkySat and Pléiades were chosen for the study due to their capability to deliver highly detailed data over several spectral bands.

Operated by Earth observation data provider Planet, SkySat consists of 21 high-resolution satellites capable of sub-daily acquisitions of panchromatic and multispectral images, at spatial resolutions of less than 1 m. The multispectral imagery used in the project included blue, green, red and near-infrared bands.

Built and operated by Airbus Defence and Space on behalf of the French space agency (CNES), Pléiades delivers optical high-resolution panchromatic and multispectral sub-metric satellite imagery with daily coverage. Panchromatic-multispectral ortho products were used in the analysis.

Complementing the space-based observations, the drone system delivered images with a spatial resolution of about 6 cm; these data covered the electromagnetic spectrum from green to near-infrared.

The subsequent analysis demonstrated that several multispectral features were able to accurately predict the disease scores from visual estimates provided by researchers on the ground.

The scientists investigated key early crop growth phases known as the booting and heading stages, which precede flowering and grain formation. It was found that visible green and red bands were more useful at identifying disease in the booting stage, with visible near-infrared vegetation indices being more important in the heading stage.

As reported in previous research, the study highlighted how the use of remote sensing technology could reduce the time, cost and person-to-person variability associated with manually scoring disease progression in crops.

Blasch concluded: “A further advantage of satellite imagery is that an expensive drone system may not be required to understand the disease progression in wheat crops; in addition, very high resolution space-borne data provides the opportunity to upscale disease detection from single field trial plots to regional and even national scales.”

Further work will augment the current methodology with machine learning and deep learning techniques, with the aim of enabling continuous monitoring systems that focus on both single and mixed rust diseases under different treatments.

References

Blasch, G., Anberbir, T., Negash, T. et al. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Sci Rep 13, 16768 (2023).
https://doi.org/10.1038/s41598-023-43770-y

Data Collections

Data from the following TPM collections were used in the research:

• SkySat full archive and new tasking
• Pleiades full archive and tasking