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Pests that spread as a result of climate change pose an increasing threat to fruit farming and viticulture in Germany. Fraunhofer researchers are working with partners to develop methods for the early identification of infestations in grapevines and in apricot, apple and pear trees so as to enable timely countermeasures to be taken. A mobile lab, the PhenoTruck®, supports rapid and reliable identification of harmful organisms directly on site. The platform provides a highly mobile system for analyzing disease symptoms, combining machine-learning methods, drone-based multispectral sensing, hyperspectral sensing and molecular biological tests.
Some pests and diseases pose such a serious threat to plants and trees that they have to be prevented from spreading right away. In the case of quarantine pests that can inflict major damage in agriculture or forestry, the infestation is subject to mandatory reporting because of the risk that they could spread into new areas. In order to identify an infection, trained personnel carry out a visual assessment that involves an examination of each individual tree and plant. These field inspections by plant health services are time-consuming and require considerable staff resources, particularly since suspected samples have to be sent to a molecular biology lab for confirmation. Working with RLP AgroScience GmbH, researchers at the Fraunhofer Institutes for Factory Operation and Automation IFF and for Biomedical Engineering IBMT have now significantly accelerated this lengthy process through the PhenoTruckAI project (see below). The PhenoTruck® is a mobile lab that will enable quarantine pest identification to be carried out exactly where growers need it, namely on site.
“As a result of climate change and global trade, dangerous non-native plant pests are continuously finding their way to Germany. Quarantine pests pose a particularly serious threat to fruit farming and viticulture,” says Fraunhofer IFF researcher Bonito Thielert. “Taking phytoplasma diseases in fruit farming and viticulture as an example, we’re validating pest detection using the PhenoTruck®—an off-road laboratory vehicle developed in this project that is equipped with specialized tools for monitoring plant diseases.” Initial tests and measurement campaigns have already been carried out in wine-growing regions in Rhineland-Palatinate and Italy, focusing on the quarantine diseases Flavescence dorée (FD), Palatinate grapevine yellows (PGY) and the disease known as Bois noir (BN), all of which affect grapevines. The mobile lab also enables researchers to analyze widely occurring diseases such as apple proliferation and pear decline, which can potentially result in complete crop loss in affected orchards.
Innovative early diagnostics directly on site
The project partners combined several technologies to be able to detect disease symptoms early on. RLP AgroScience GmbH contributed its agricultural expertise and performed the molecular biological analyses, while Fraunhofer IFF provided expertise in AI-optimized monitoring and hyperspectral analysis. Fraunhofer IBMT was responsible for the design, development, integration and implementation of the mobile PhenoTruck® platform.
As a first step, drones equipped with multispectral sensors are deployed for automated, large-scale monitoring, with the multispectral images enabling the survey of large cultivation areas. In addition, affected zones are documented using a specially developed field assessment app. Symptomatic leaves are then analyzed in the PhenoTruck® by means of hyperspectral cameras that rapidly detect and localize suspicious discoloration. Thielert: “Early leaf discoloration is a key indicator of phytoplasma diseases. When a plant shows symptoms in a leaf sample in the lab, hyperspectral analysis reveals these in defined wavelength ranges with greater clarity and consistency than would be possible based on visible color changes alone.”
AI as a key technology for analyzing disease symptoms
The next step involves the use of artificial intelligence methods for fast and reliable analysis of the captured data. “Our AI models are able to detect phytoplasmosis with a high degree of certainty, achieving accuracy rates of 95 to 99 percent. One particular feature is the AI’s ability to process datasets automatically.” The researchers also trained the AI models to differentiate between the relevant grapevine phytoplasma diseases, namely Flavescence dorée (FD), Bois noir (BN) and Palatinate grapevine yellows (PGY). This is important because BN and PGY are not as dangerous as FD: Unlike FD they do not spread epidemically from vine to vine. Although the symptoms of these diseases are very similar and often confused with one another, BN and PGY pose a lower risk due to their slower and more limited spread. The phytoplasma diseases were distinguished with 80 percent accuracy.
The preliminary selection is made based on hyperspectral analysis, and samples suspected of infection are then examined directly on site by means of molecular biological testing in the PhenoTruck® lab. This is because phytoplasma diseases in grapevines and fruit trees can only be confirmed with certainty using molecular methods. A rapid test developed specifically for the mobile lab (LAMP) takes one hour to complete, so it is somewhat faster than a PCR analysis. Given the platform’s wide range of applications, the aim is to use the PhenoTruck® for research and development in the future.
Project PhenoTruckAI
Mobile lab for the rapid and reliable identification of quarantine pests in agriculture
Project duration:
September 2021–September 2025
Project and collaboration partners:
• RLP AgroScience, now DLR Rheinland-Pfalz (project coordination, visual and molecular pathogen diagnostics)
• Fraunhofer IBMT (implementation of the PhenoTruck®)
• Fraunhofer IFF (AI-based analysis and evaluation of hyperspectral data, drone data analysis and development of a field assessment app)
Project funded by:
German Federal Ministry for Food and Agriculture
Project sponsor:
German Federal Office for Agriculture and Food (BLE)
https://www.fraunhofer.de/en/press/research-news/2026/january-2026/phenotruck-mo...
Pests that spread as a result of climate change pose a threat to viticulture in Germany.
Copyright: © Fraunhofer IFF
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Pests that spread as a result of climate change pose a threat to viticulture in Germany.
Copyright: © Fraunhofer IFF
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