Du hast keine Chance, aber nutze sie. Vom Pariser Frühling zum Pariser Herbst. Zur Bedeutung der Schülerrevolte im November 1990

Der Beitrag berichtet über die Schülerrevolte in Frankreich 1990. Die von den Demonstranten eingeklagten Maßnahmen lassen auf den Wunsch nach mehr Disziplin und Effizienz schließen: Einstellung von mehr Lehrern mit geringerem Stundendeputat, die, auf diese Weise entlastet, einen konzentrierteren Unterricht gewährleisten könnten; mehr Verwaltungspersonal für einen reibungslosen Ablauf der administrativen Prozesse; mehr finanzielle Mittel für die Anschaffung von Büchern und Materialien. Zwar wurde auch der Ruf nach größerer Redefreiheit in den Schulen und nach pädagogischen Reformen laut, er verhallte aber ungehört neben den pragmatischen Forderungen. (DIPF/Orig.

Primarily tests of a optoelectronic in-canopy sensor for evaluation of vertical disease infection in cereals

BACKGROUND: Health scouting of crops by satellite, airplanes, unmanned aerial (UAV) and ground vehicles can only evaluate the crop from above. The visible leaves may show no disease symptoms, but lower, older leaves not visible from above can do. A mobile in-canopy sensor was developed, carried by a tractor to detect diseases in cereal crops. Photodiodes measure the reflected light in the red and infrared wavelength range at 10 different vertical heights in lateral directions. RESULTS: Significant differences occurred in the vegetation index NDVI of sensor levels operated inside and near the winter wheat canopy between infected (stripe rust: 2018, 2019 / leaf rust: 2020) and control plots. The differences were not significant at those sensor levels operated far above the canopy. CONCLUSIONS: Lateral reflectance measurements inside the crop canopy are able to distinguish between disease-infected and healthy crops. In future mobile in-canopy scouting could be an extension to the common above-canopy scouting praxis for making spraying decisions by the farmer or decision support systems. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry

Methoden zur Erkennung des Kartoffelkäfers (Leptinotarsa decemlineata (Say)) mit Multispektral- und Farbbildkamera-Sensoren

At the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides). In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed

Ein pixel-basiertes Segmentierungsmodell zur Identifizierung von Hunds-Kerbel (Anthriscus caucalis M. Bieb.) in Farbbildern eines Getreidefeldes

Because of insufficient effectiveness after herbicide application in autumn, bur chervil (Anthriscus caucalis M. Bieb.) is often present in cereal fields in spring. A second reason for spreading is the warm winter in Europe due to climate change. This weed continues to germinate from autumn to spring. To prevent further spreading, a site-specific control in spring is reasonable. Color imagery would offer cheap and complete monitoring of entire fields. In this study, an end-to-end fully convolutional network approach is presented to detect bur chervil within color images. The dataset consisted of images taken at three sampling dates in spring 2018 in winter wheat and at one date in 2019 in winter rye from the same field. Pixels representing bur chervil were manually annotated in all images. After a random image augmentation was done, a Unet-based convolutional neural network model was trained using 560 (80%) of the sub-images from 2018 (training images). The power of the trained model at the three different sampling dates in 2018 was evaluated at 141 (20%) of the manually annotated sub-images from 2018 and all (100%) sub-images from 2019 (test images). Comparing the estimated and the manually annotated weed plants in the test images the Intersection over Union (Jaccard index) showed mean values in the range of 0.9628 to 0.9909 for the three sampling dates in 2018, and a value of 0.9292 for the one date in 2019. The Dice coefficients yielded mean values in the range of 0.9801 to 0.9954 for 2018 and a value of 0.9605 in 2019

Teilflächenspezifisches Herbizidsplitting in Feldmöhren entsprechend sensorbasierter Erfassung der Verunkrautung

Die Bereitstellung von Speisemöhren für den deutschen Markt erfolgt zu einem nicht unerheblichen Anteil aus der Inlandsproduktion im Freiland. Um eine optimale Herbizidwirkung bei minimaler Schädigung der Möhrenpflanze zu erzielen, hat sich ein bis zu dreimaliges Splitting der empfohlenen Höchstmenge bewährt. Da in den spezialisierten Betrieben teilweise hohe Flächenkonzentrationen erreicht werden, besteht bei einer der Unkrautdichte angepassten Herbizidapplikation ein hohes Potenzial zur Reduzierung des Einsatzes von Pflanzenschutzmitteln.In Streifenversuchen sollte die Wirkung eines teilflächenspezifischen Herbizidsplittings auf die Spätverunkrautung sowie auf verschiedene Ertragsparameter getestet werden. Art und Anzahl der Unkräuter wurden im Frühjahr mittels manueller Bonituren entlang eines Stichprobengitters erfasst. Danach erfolgte im 3-Blattstadium der Möhren mit einem am Leibniz-Institut für Agrartechnik (ATB) entwickelten Kamerasensor die kleinräumige Detektion der auf dem Feld vorhandenen Verunkrautung. Die Sensorwerte waren die Grundlage für das Erstellen von Unkrautverteilungskarten. Anhand dieser Verteilungskarten wurden in dem der Unkrautverteilung angepassten Applikationsstreifen drei Zonen mit Aufwandmengen von 200 L ha-1, 300 L ha-1, und 400 L ha-1 gebildet. In den beidseitig benachbarten Streifen erfolgte ein flächeneinheitliches Splitting mit der betriebsüblichen Menge von 400 L ha-1. Die drei Applikationszonen wurden während des Splittings bei der zweiten bzw. dritten Herbizidanwendung beibehalten. An jedem der zwei bzw. drei Spritzzeitpunkte konnten durch das teilflächenspezifische Splitting 16 % (2005) und 20 % (2006) des Pflanzenschutzmittels gegenüber einem flächeneinheitlichen Splitting eingespart werden.Zur Beurteilung der Ertragswirksamkeit wurden in den 2 Applikationsvarianten an jeweils gegenüberliegenden Stichprobenpunkten die Möhren von Hand geerntet und die Ertragsparameter Gewicht sowie Anzahl „gesamt“ bzw. „vermarktungsfähig“ bestimmt. Mit Hilfe der Differenzenmethode (lokale Ertragsparameterwerte teilflächenspezifisch minus einheitlich) erfolgte der statistische Vergleich abhängiger Stichproben mit dem t- Test. Unterstellt man eine Irrtumswahrscheinlichkeit von α = 5 % wurden in 19 der insgesamt 24 Tests keine statistisch gesicherten Unterschiede der Ertragsparameter zwischen den zwei Behandlungsvarianten in den jeweiligen Applikationszonen gefunden. Die Nachverunkrautung war in beiden Varianten sehr gering und damit vernachlässigbar.Stichwörter: Applikationskarte, Kamera, Möhren, Präzise Unkrautkontrolle, SensorSite-specific herbicide splitting in field carrots based on camera detected weed infestationAbstractThe production of carrots for the German market comes mainly from domestic production. To ensure the efficiency of chemical weed control and to minimize the damage of the crop a splitting of the recommended dosage up to three times is often practiced. Because of large field areas of the carrot cropping and processing enterprises, the potential to save herbicides by practicing an herbicide application adapted to the weed occurrence is high.The efficiency of a site-specific herbicide splitting on the late weed occurrence as well as on yield parameters was tested in field strip trials. Weed species and abundance were determined manually by raster sampling using a counting frame in spring before spraying. Afterwards in the three leaf growth stages of the carrots the weed coverage level was detected online using a camera sensor developed by the Leibniz Institute for Agricultural Engineering (ATB). Based on weed coverage level maps three application zones (200 L ha-1, 300 L ha-1, 400 L ha-1) were defined. On both sides of the site specific splitting strip a uniform splitting strip (400 L ha-1) was applied. The position of the application zones were the same during site-specific splitting at the second and third herbicide spraying respectively. Compared to a conventional uniform splitting herbicide savings were 16% (2005) und 20% (2006) at each spraying time.To evaluate the efficacy of the site-specific splitting on the yield manually harvesting were performed at opposite points in both treatments. The yield parameters fresh weight and numbers of carrots “total” and “marketable” were determined. Assuming a significance level of α = 5% the difference method for controlled treatment comparison in large scale field trials (t-test) resulted in 19 of the 24 tests in total no differences between the treatments. The late weed occurrence in both treatments was low.Keywords: Application map, camera, carrots, precise weed control, senso

Hyperspektrale Bildanalyse zur Unterscheidung von Ambrosia artemisiifolia und Tagetes ssp.

Zur Verhinderung der weiteren Verbreitung der Beifußblättrigen Ambrosie müssen Nester und Einzelpflanzen lokalisiert und bekämpft werden. Kamerasensoren, die berührungslos arbeiten und leicht an landwirtschaftliche und kommunale Fahrzeuge anzubringen sind, wären eine geeignete Technik, um große Landschaftsareale zu scannen. In Kleingärten kämen eher preiswerte Handmessgeräte in Betracht. Effektive Monitoringstrategien auf der Basis optischer Methoden setzen spektrale Unterschiede im Reflexionsverhalten von Ambrosia artemisiifolia L. gegenüber anderen Pflanzenarten voraus. Es wurde ein hyperspektraler Scanner entwickelt, um die Reflexionseigenschaften von Pflanzen zu analysieren. Beifuß-Ambrosien- und Tagetes-Pflanzen wurden an vier Terminen (bis zur Blüte) gescannt. Aus den Hyperspektralbildern erfolgte die Generierung der Referenzspektren ausgewählter Regionen von Blatt und Stiel beider Pflanzenarten mit der Software ENVI. Eindeutige spektrale Unterschiede in den Blättern beider Pflanzenarten waren an allen vier Messterminen nicht zu erkennen. Unterschiede traten hinsichtlich der Stiele der zwei Pflanzenarten besonders an den zwei ersten Messterminen auf. Die Reflexion der Stiele beider Pflanzenarten nahm im Gegensatz zu den Blättern, mit Ausnahme der Tagetes-Stiele am zweiten Termin, vom Grünbereich (550 nm) zum Rotbereich (640 nm) zu. An den beiden letzten Terminen war der Verlauf der Spektren von Blättern und Stielen wieder ähnlich. Eine Unterscheidung der zwei Pflanzenarten unter Verwendung dieser zwei Wellenlängen gelang nicht.Stichwörter: Ambrosia artemisiifolia, hyperspektrale Bildanalyse, Pflanzenerkennung, Tagetes spp.Hyperspectral image analysis for discrimination of Ambrosia artemisiifolia and Tagetes ssp.To avoid a further spreading of ragweed, patches as well as single plants have to be located and destroyed. Camera sensors can operate contactless and can easily be fixed to farm and urban machines to scan huge landscape areas. In private gardens cheap hand held devices would come into consideration. Effective monitoring strategies based on optical methods imply spectral differences in the reflection behavior. A hyperspectral line scanner was developed to analyze the reflection properties of plants. Single plants of common ragweed and Tagetes were scanned four times (till flowering). With the software ENVI reference spectra for stems and leaves were generated separately. At all four measurement times no unique spectral differences of the leaves of both plant species were visible. But there were differences regarding the stems especially at the first and second measuring. In contrast to the leaves, the reflection of the stems was increasing from green (550 nm) to red (640 nm) with the exception of marigold stems on the second measuring time. At the third and fourth time the spectra of leaves and stems were similar. A discrimination of the two species by using these two wavelengths did not succeed.Keywords: Ambrosia artemisiifolia, hyperspectral image analysis, plant discrimination, Tagetes spp

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks

Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops

Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h−1 area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields