Advanced Multi-color Fluorescence Imaging System for Detection of Biotic and Abiotic Stresses in Leaves

The autofluorescence of a sample is a highly sensitive and selective optical property and gives the possibility to establish non-destructive techniques of the investigation of plants, like detecting the chlorophyll fluorescence related to stress phenomena. In this study, an advanced multi-color fluorescence imaging system and data analysis were presented. The advantage of an imaging system is the additional receiving of spatial information over a sample area, this is a strong improvement compared to spot measurements commonly used. The purpose was to demonstrate the possibility of the detection and characterization of stress symptoms using this system. Specific fluorescence ratios were identified to characterize the stress status over the whole leaf, here shown on barley grown under different nitrogen supply (abiotic stress). Due to the changes, it is possible to make conclusions about leaf pigments (chlorophylls and phenolics) related to stress response. The second aim was to use the shape of local symptoms (biotic stress) as a criterion. For this purpose, three structural different kinds of fungal symptoms were analyzed using shape descriptors. It shows that an additional image shape analysis can be very useful for extracting further information, in this case the successful discrimination of fungal infections

CLOSYS: Closed system for water and nutrient management in horticulture

The EU project CLOSYS aimed at developing a CLOsed SYStem for water and nutrients in horticulture. The main objective was to control water and nutrients accurately such that pollution is minimized and crop quality enhanced. The closed system as developed in this project consists of crop growth models and substrate models, a new substrate, an expert system, a real time controller, fluorescence sensors, ion-selective sensors and a technical infrastructure. Plant model: Mechanistic models for rose and sweet pepper were build and self-learning capacity was introduced. The models simulate crop growth, and demand and uptake of water and individual nutrients. Plant sensor: A fluorescence imaging system was developed and tested to be used as an indicator for plant performance and stress factors. Nutrient sensor: An on-line multi-ion sensor measures the concentration of individual nutrients pH and EC of the recirculating water in the greenhouse. Substrate model: A 3D substrate model simulates the water and nutrient flows in the substrate depending on the root absorption and fertigation. Substrate: A rockwool substrate with improved physical and chemical properties was developed to allow a better control of water and nutrient fluxes in the root-zone. Expert system: The expert system, using model and sensor information and weather forecasts, determines a daily plan for fertigation. This plan contains the set-points for the real time controller. Real time controller: The real time controller controls the water and nutrient supply. It upgrades the fertigation parameters (irrigation EC, dose and frequency) to satisfy the set-points issued by the expert system, depending on current status of the system and on time constants and dynamic characteristics of the system. Technical infrastructure: All subsystems were integrated such that they can request data from the irrigation computer database. With these data, new set points for fertigation are calculated, whereafter the irrigation computer executes the requested tasks. Closed system: All components together form the closed system for water and nutrients. The performance of the closed system was compared to a standard sweet pepper growing system. The system has been running satisfactorily during a prolonged period (1 and a half year). Water and nutrient use, its availability in the rooting zone as well as the recirculating drainage water were controlled accurately