Lutz Damerow

UV-induced fluorescence spectra and lifetime determination for detection of leaf rust (Puccinia triticina) in susceptible and resistant wheat (Triticum aestivum) cultivars

In modern agriculture, the use of cultivars that are resistant against specific stresses, e.g. pathogen infections, is an integral component. Considering the great demand for a rapid and objective screening method for stress resistance of new cultivars, the question arises, whether time resolved fluorescence spectroscopy is suitable for such purposes. Amongst others, infected plants might accumulate specific compounds such as salicylic acid and phenylpropanoid compounds as key substances in plant disease resistance, whereas synthesis and accumulation may influence fluorescence parameters such as absolute intensity of single peaks, ratios between peaks and lifetime. Experiments were conducted in a controlled-environment cabinet cultivating four leaf rust susceptible and three leaf rust resistant genotypes. Fluorescence measurements were conducted using a compact fibre-optic fluorescence spectrometer with a nanosecond time-resolution. Results of experiments revealed that UV-induced measurements of spectral characteristics as well as determination of fluorescence lifetime are suited to detect leaf rust (Puccinia triticina) in wheat (Triticum aestivum L.) cultivars as early as 2 days after inoculation (dai). For this purpose several parameters such as the fluorescence (F) amplitude ratios F451/F522, F451/F687, F451/F736, F522/F687, F522/F736 as well as fluorescence mean lifetime especially at 470nm, might be used. Discrimination between resistant and susceptible cultivars to the leaf rust pathogen could be accomplished 3dai by using the ratio of fluorescence amplitude between the blue (F451nm) and red (F687nm) peak, and mean lifetime at 440, 500 and 530nm. Our results indicate that the combination of spectrally and time-resolved fluorescence could be an additional tool in plant breeding programs for an automatic and precise high-throughput system for evaluation of the pathogen resistance of new genotypes.

Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

(1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

Non-Invasive Examination of Plant Surfaces by Opto-Electronic Means—Using Russet as a Prime Example

(1) Background: Many disorders and diseases of agricultural produce change the physical features of surfaces of plant organs; in terms of russet, e.g., of apple or pear, affected fruit peel becomes rough and brown in color, which is associated with changes in light reflection; (2) Objective and Methods: The objective of the present project was an interdisciplinary approach between horticultural science and engineering to examine two new innovative technologies as to their suitability for the non-destructive determination of surfaces of plant organs, using russet as an example, and (a) an industrial luster sensor (type CZ-H72, Keyence, Japan) and (b) a new type of a three-dimensional (3D) color microscope (VHX 5000); (3) Results: In the case of russet, i.e., suberinization of the fruit peel, peel roughness increased by ca. 2.5-fold from ca. 20 µm to ca. 50 µm on affected fruit sections when viewed at 200× magnification. Russeted peel showed significantly reduced luster, with smaller variation than russet-devoid peel with larger variation; (4) Conclusion: These results indicate that both sensors are suitable for biological material and their use for non-contact, non-invasive detection of surface disorders on agricultural produce such as russet may be a very powerful tool for many applications in agriculture and beyond in the future.

The wax bloom on blueberry: Application of luster sensor technology to assess glossiness and the effect of polishing as a fruit quality parameter

The wax bloom of the fruit is responsible for the visible quality of blueberries. This study aimed to investigate a new technology using the effect of polishing on micromorphology, wax content and weight loss of blueberries. Luster sensor (type CZ-H72, Keyence, Japan) technology was used to assess glossiness of polished blueberries compared with berries with a natural (unpolished) wax layer during 9 days after harvest. Blueberries were rubbed twice by hand within a soft microfibre tissue to obtain polished fruit. Unpolished blueberries contained ca. 120 μg wax cm-2, which was reduced by ca. 22% to ca. 95 μg cm-2 by polishing. This reduction was associated with an increase in luster levels from ca. 65 to 80 a.u.. Weight loss was larger from polished than from unpolished blueberries with a concomitant 40% increase in luster levels from 60 to 85 a.u. in polished fruit. Luster levels sharply decreased from 85 a.u. in the first 5 days after harvest and then leveled off to remain almost constant at ca. 20 a.u. with significantly larger values for polished blueberries of ca. 30 a.u. with a larger magnitude of glossiness. Overall, luster sensor technology may offer a new effective, affordable, possibly portable, non-destructive technique to assess glossiness or other surface features in real time for classifying not only blueberry, but also other waxy fruit such as aubergine/eggplant, plum, Juniperus, blue grape berry etc..

Bestimmung der Korngeometrie der Durchwachsenen Silphie als Voraussetzung für die Einzelkornsaat

In search of economically and ecologically interesting alternatives and additions to maize, currently the predominantly used biogas substrate, cup plant (Silphium perfoliatum L.) has been found as a promising plant species. The perennial composite, which is located in North America, can be established by modified conventional seeders. Due to a long flowering period and different geographic origins of the plant material, seeds of different sizes and shapes can be found in the seed batch, which makes a precise singling of the seeds difficult. For the quantification of the geometric parameters the seeds were recorded and measured. The results of these researches were used to optimize the seeding in precision seeders.

Kameragesteuerte mechanische Unkrautbekämpfung in Pflanzenreihen

Am Institut fur Landtechnik der Universitat Bonn wurde ein Anbaugerat zur mechanischen Unkrautkontrolle von Reihenkulturen entwickelt und getestet. Ziel war es, den Bereich zwischen den Kulturpflanzen in der Reihe (intra-row) zu bearbeiten, ohne diese zu beschadigen. Als Schwerpunkt des Forschungsprojektes wurde die Pflanzenerkennung mithilfe einer kamerabasierten Bildverarbeitung entwickelt. Dabei sollten standardisierte Industriekomponenten und Kommunikationsstandards verwendet werden. Fur den Antrieb der Hackwerkzeuge wurden elektrische Servomotoren gewahlt. Die Ansteuerung erfolgt uber ein echtzeitfahiges EtherCAT- Bussystem, welches an einen Echtzeit-Controller angeschlossen ist. Dieser Aufbau ermoglicht die mechanische Unkrautbekampfung bis zu einer Fahrgeschwindigkeit von 7,2 km/h.

Bestimmung der Porosität von Böden durch Messung der Oberflächenrauhigkeit

Die Bodenbearbeitung zielt auf eine fur die Einbettung und den Auflauf von Samen gunstigeStruktur der Oberflache ab. Die Oberflachenrauhigkeit und Oberflachenporositat sind zwei Grosen, die die physikalischen Bedingungen der Oberflache beschreiben und deshalb das Ergebnis der Bodenbearbeitung reprasentieren. Eine kurzfristige Ermittlung dieser beiden Parameter erlaubt daher eine Beurteilung der Qualitat der Bodenbearbeitung. Wahrend die Oberflachenrauhigkeit mit einem Relief-Laserscanner im Feld durchgefuhrt werden kann, muss die Porositat im Labor bestimmt werden. Die Untersuchung befasst sich deshalb mit dem Zusammenhang beider Grosen und ergibt in einem mittelfristigen Zeitraum (< 3 Wochen), der fur die Saatbettbereitung von vorrangiger Bedeutung ist, eine hohe Korrelation zwischen der Oberflacherauhigkeit und der Oberflachenporositat.