Multicolour Fluorescence Imaging of Sugar Beet Leaves with Different Nitrogen Status by Flash Lamp UV-Excitation

Langsdorf, Gabriele; Buschmann, Claus; Sowińska, M.; Babani, Fatbardha; Mokry, M.; Timmermann, F.; Lichtenthaler, H. K.

doi:10.1023/a:1012409423487

Cited by 49 publications

(23 citation statements)

References 53 publications

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“…BGF is well known to be influenced by the nutrient content in soil. Several authors reported changes in BGF related to alterations in the phenolics content in plants upon treatments with low or high nitrogen, depending on the species (Heisel et al, 1996; Langsdorf et al, 2000). MCFI has also been used in a number of works analyzing the effect of pathogens in their hosts.…”

Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected.” The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

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Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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“…Unlike reflectance indices, the fluorescence spectra are less affected by biomass or leaf area index [15,21]. At different N nutritional levels, the fluorescence intensities of leaves are significantly different near the 440 nm (Blue, B), 525 nm (Green, G), 685-690 nm (Red, R), and 735-740 nm (Near-infrared, NIR) wavelengths [22,23]. Studies have shown that the fluorescence ratio of NIR and R bands is highly correlated with chlorophyll concentration [24,25] and leaf N concentration [26].…”

Section: Introductionmentioning

confidence: 99%

In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

et al. 2019

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Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex ® 3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex ® 3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue-green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R 2 ) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_R NSI ) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_R NSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.3 of 21 their photoprotective roles [31]. These compounds are mainly concentrated in epidermal cells and have typical absorption peaks in the ultraviolet region [31][32][33]. Thus, N status diagnosis is improved by combining the polyphenol and chlorophyll fluorescence [34,35]. Lejealle et al. [36] demonstrated that the N balance index (NBI), the ratio of chlorophyll to flavonol, had a better and more stable correlation with leaf N concentration. Leaf fluorescence sensor Dualex (FORCE-A, Orsay, Paris, France) and canopy fluorescence sensor Multiplex (FORCE-A, Orsay, Paris, France) can be used to estimate plant polyphenol conte...

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“…In a number of experiments with different stress treatments applied to rose and sweet pepper plants the relation between plant status and the signals of the fluorescence imaging sensor was established. It was demonstrated that fluorescence ratios were most suitable to distinguish between different stress treatments (Langsdorf et al, 2000). The fluorescence ratios blue/red and blue/far red were increased by nitrogen deficiency but not by iron deficiency (Fig.…”

Section: Plant Sensormentioning

confidence: 93%

Closys: Closed System for Water and Nutrient Management in Horticulture

Marcelis¹,

Dieleman²,

Boulard³

et al. 2006

Acta Hortic.

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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 setpoints 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. INTRODUCTIONIn greenhouse horticulture nutrients are usually supplied together with water. To prevent any shortage growers use excess amounts of nutrients and water. Even in the case a recirculation system is used, growers regularly drain nutrients and water to the environment to prevent imbalances of the nutrient solution in the root zone. As in present production systems nutrients and water are always supplied in excess amounts, possibilities to control and to plan crop growth and product quality by a regulated water and nutrient supply are hardly used by growers. In greenhouses, compared to open field production, a...

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Multicolour Fluorescence Imaging of Sugar Beet Leaves with Different Nitrogen Status by Flash Lamp UV-Excitation

Cited by 49 publications

References 53 publications

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

Closys: Closed System for Water and Nutrient Management in Horticulture

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