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The detection of Alternaria in potato crops using drone images 

Apr 29, 2024 | Blog, CODECS News

Authors: Reinout Godaert and Jonathan Van Beek from ILVO 

The Horizon Europe project CODECS aims to improve the motivation and the capacity of European farmers to understand and adopt digitalisation as an enabler of sustainable and transformative change. CODECS has established a network of 21 Living Labs, consisting of farmers, knowledge intermediaries, stakeholders, and policymakers, to address emerging agricultural challenges. These Living Labs will take a system-level approach to analyse farms and evaluate the impact of digital technologies on economic, social, and environmental outcomes.  

ILVO’s Agrifood Technology is one of these Living Labs and is testing ultra-high spatial resolution RGB imagery taken from  drone platforms, AI models, task map generation and label tools for weed, disease and pest detection in commercial farms of arable crops. Under the farm to fork strategy, part of the European Green Deal, the EU has set a target of a 50% reduction in the overall use of chemical plant protection products by 2030. With the help of aforementioned digital solutions, the Living Lab wants to provide farmers with the tools to only spray where it’s needed. This measure would help ensure that farmers can meet the objectives set by Europe without sacrificing yield. 

Drone imaging for disease detection in potato crops 

In 2023, a number of pilot projects were carried out to test these technologies. In these pilot projects, drone imagery was used for the monitoring and early detection of Alternaria in potato crops. Alternaria solani is the second most devastating foliar pathogen of potato crops worldwide. It causes early leaf diseases and premature loss of plant leaves, and therefore negatively impacts the yield. Hence, preventive, pre-symptomatic and full-field use of crop protection products is currently used to avoid infections. 

However, earlier research by Ruben Van De Vijver and Jana Wieme showed that camera technology can be used to detect symptoms at an early stage (3-5 days after infection). To achieve this, a modified RGB camera equipped with a near-infrared band (part of the electromagnetic spectrum undetectable by the human eye) was employed, providing enhanced contrast between healthy and damaged parts of the leaf.  

By taking into account where and when symptoms occur, the use of crop protection products can be limited to the areas and times when they are really needed.  

In the pilot projects, the possibilities of drone technology for Alternaria detection, along with its advantages and disadvantages were explained to farmers. Furthermore, pilots underwent training to conduct drone flights in a qualitative manner and become familiar with the processing software and the Alternaria detection algorithm. In addition, the companies  received guidance and assistance in interpreting the results. After completion of the pilot projects, the various aspects could be evaluated and improvements were recorded for future campaigns.  

Conclusions 

The use of drone imagery to provide advice on drone detection and crop protection further supports the decision-making process of farmers. It enables the farmer to confirm which specific areas of the field require treatment and the site-specific task map can then be loaded directly into the spraying machine. The pilot projects revealed that certainly not everything went smoothly. However, with effective communication and meticulous planning between farmers and drone pilots is crucial. Despite challenges, the results showed that it was possible to transmitting the data, processing it and make the resulting application map available to the end user within a few hours of conducting the flight. 

If you would like to have more information, you can contact Reinout Godaert () and Jonathan Van Beek (

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