Needs-based and reduced use of pesticides

Agriculture is undergoing a profound transformation in which economic, ecological, and societal interests must be balanced under the influence of climate change and geopolitical crises. Regulatory requirements—such as the EU’s goal to halve the use of chemical plant protection products (PPPs) by 2030—pose major challenges for farmers, as alternative strategies for combating plant diseases are not yet fully developed. Excessive use of fungicides leads to loss of biodiversity, while their absence can result in significant yield losses. The medium-term goal must therefore be to optimally adjust the amount of PPPs to the actual need, applying them only when a pathogenic infection is present or imminent. To achieve this, new and innovative methods must be developed to detect infections as early as possible or to predict them using intelligent forecasting models.

Currently, plant pathogen detection relies either on visual inspection or time-consuming laboratory analyses. While molecular methods such as PCR or ELISA offer high sensitivity, they are not suitable for practical field use. Rapid tests like lateral flow assays (LFA) can be applied on-site but are often not sensitive enough to detect infections at an early stage.

The collaborative project “MagnI-SENSE” aims to develop an innovative analysis and monitoring system that integrates on-site analytics, an individualized advisory concept, and AI-assisted prediction models for infection detection. This will enable farmers to significantly reduce the use of plant protection products, maximize crop yields, and thereby achieve both ecological and economic benefits through lower costs and more sustainable farming practices.

Under the coordination of Fraunhofer IME, MagnI-SENSE is investigating four technical innovations that together enable a more precise prediction of pathogen occurrence and provide actionable recommendations for the targeted use of plant protection products (PPPs):

1. MagnI-SEP: A simple, statistically grounded method for sampling and on-site sample preparation and enrichment using special magnetic nanosensors that specifically concentrate pathogens.

2. MagnI-TECT: A fast and straightforward on-site assessment of pathogen load through magnetic immunodetection in a dedicated reading device, providing both qualitative and quantitative results.

3. MagnI-LAMP: A magnetic detection method based on loop-mediated isothermal amplification (LAMP), enabling highly sensitive mobile detection of specific gene sequences for multiple pathogen groups simultaneously.

4. MagnI-APP: On-site collected analytical data are linked with other external information in a cloud-based database. An AI-supported monitoring algorithm processes these data and provides users with predictions of pathogen outbreaks as well as targeted recommendations for action.

The four innovations are being tested and demonstrated in practice using selected fungal pathogens. Due to the lack of detection methods until now, these pathogens often go unnoticed during the early and spread phases, causing significant yield losses to affected plants. MagnI-SENSE aims to address this problem effectively and provide farmers with targeted recommendations to promote plant health. A key aspect of the project is the active involvement of key stakeholders in the agricultural value chain, ensuring that the system meets the needs of end users.