Plantenna Demonstrator: novel sensors for monitoring plant health

Measuring plant-balance and -water dynamics is essential to gain better insight into plant health. In the Plantena research program (https://www.4tu.nl/plantenna/en/), new techniques have been developed for direct monitoring of plant traits. These include water status monitoring based on Ultrasound, magnetic resonance imaging (MRI) or radiofrequency (RF), volatile compound emission (“e-Nose”) and continuous stomatal aperture sensing (SAS). The SAS sensor enables real-time autonomous imaging of stomatal apertures inside the growth environment to asses dynamic behavior of individual stomata within the ensemble. For e-Nose, a new approach is being explored to utilize an electronic nose to smell insects for early detection of pests, thus safeguarding crop harvests while minimizing pesticide usage. RF sensing , Ultrasound and MRI are non-invasive techniques for real-time monitoring of internal plant parameters. RF is being investigated for monitoring of water and mineral content, Ultrasound technology enables the determination of internal plant parameters in a fast, non-contact, and non-destructive matter, thereby providing new ways for water monitoring, pest detection, and selective breeding. MRI enables monitoring of water content and flow in plants and offers the potential for non-invasive metabolite detection.

Additionally, low-cost, autonomous sensor nodes are being developed for integration of novel and existing plant-sensors into a high density network (“internet of plants”). To this end, a smart and efficient power management scheme is being developed to adapt the sensor nodes to a wide range of environmental scenarios. A first demonstration of sensor innovations will be set up in spring 2022 in a commercial greenhouse environment. In this contribution we will present preliminary results of novel plant-sensors as well as the set-up of the Plantenna Demonstrator facility.

Outlook: Results of the Plantenna Demonstrator will validate performance of the plant-sensor innovations in a real-life environment and by combining these with existing sensors will provide valuable datasets for assessing plant response to climate variability and stress conditions.