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Author: Dammer, Karl-Heinz × DDC: 630 ×

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Now showing 1 - 7 of 7
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    Zielflächenorientierte, präzise Echtzeit-Fungizidapplikation in Getreide
    (Darmstadt : KTBL, 2015) Dammer, Karl-Heinz; Hamdorf, André; Ustyuzhanin, Anton; Schirrmann, Michael; Leithold, Peer; Leithold, Hermann; Volk, Thomas; Tackenberg, Maria
    Im Rahmen eines Verbundprojektes wurden Echtzeit-Applikationstechnologien mit berührungslosen Sensoren für präzise Fungizid-Spritzungen in Getreide entwickelt. Das Entscheidungshilfe- System proPlant expert.classic bzw. die Internetversion proPlant expert.com (proPlant GmbH) empfiehlt geeignete Fungizide und Dosierungen für ein bestimmtes Infektionsszenario der acht wichtigsten Blatt- und Ährenkrankheiten von Winterweizen. Das Precision- Farming-Modul „Fungizid“, welches auf dem Terminal in der Traktorenkabine läuft, steuert das präzise Spritzverfahren. Das Modul bestimmt die lokale Zielapplikationsmenge während des Spritzens durch Nutzung des lokalen Ultraschallsensorwerts als Eingabeparameter. In den Jahren 2013 und 2014 wurden Feldversuche in Winterweizen durchgeführt, um die Beziehung zwischen den Sensorwerten (Ultraschall- und Kamerasensor) und den Pflanzenparametern Pflanzenoberfläche (Leaf Area Index, LAI) sowie Biomasse zu analysieren. Diese sind für einen örtlich angepassten variablen Fungizideinsatz zur Bemessung der Spritzmenge wichtig. Die Messungen wurden mehrmals während der Vegetationsperiode an visuell ausgewählten Stichprobenpunkten entsprechend der unterschiedlichen Bestandsdichte durchgeführt. Nach Änderungen an der Sensortechnik konnten für 2014 signifikante lineare Regressionsmodelle zur Beschreibung der Beziehung zwischen den Sensorwerten und den zwei Pflanzenparametern LAI sowie Biomasse gefunden werden.
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    Vegetationserkennung für landwirtschaftliche Anwendungen mithilfe einer Ein-Chip-Kamera
    (Darmstadt : KTBL, 2014) Selbeck, Jörn; Dworak, Volker; Hoffmann, Matthias; Dammer, Karl-Heinz
    Durch die Anwendung von Kameras bei der Prozesskontrolle in der Präzisionslandwirtschaft können Dünger, Pestizide, Maschinenzeit und Treibstoff eingespart werden. Trotz der hohen Forschungsaktivitäten auf diesem Gebiet verhindern hohe Preise für geeignete Kamerasysteme die Anwendung in allen Bereichen der Landwirtschaft. Intelligente und kostengünstige Kameras, die für landwirtschaftliche Anwendungen angepasst werden, können diesen Nachteil überwinden. Der normalisierte differenzierte Vegetationsindex (NDVI) ist ein Algorithmus in der Bildanalyse zur Trennung von Pflanze und Boden (Hintergrund) und wird in der hier vorgestellten Untersuchung bei einer kostengünstigen Ein-Chip-Kamera implementiert und angepasst.
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    A Pixel-wise Segmentation Model to Identify Bur Chervil (Anthriscus caucalis M. Bieb.) Within Images from a Cereal Cropping Field
    (Berlin ; Heidelberg : Springer, 2022) Karimi, Hadi; Navid, Hossein; Dammer, Karl-Heinz
    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.
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    Methods for Recognition of Colorado Beetle (Leptinotarsa decemlineata (Say)) with Multispectral and Color Camera-sensors
    (Berlin ; Heidelberg : Springer, 2022) Dammer, Karl-Heinz
    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.
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    Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems
    (Basel : MDPI, 2013) Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe
    The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.
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    Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery
    (Basel : MDPI, 2017) Schirrmann, Michael; Hamdorf, André; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Dammer, Karl-Heinz
    A crop height model (CHM) can be an important element of the decision making process in agriculture, because it relates well with many agronomic parameters, e.g., crop height, plant biomass or crop yield. Today, CHMs can be inexpensively obtained from overlapping imagery captured from unmanned aerial vehicle (UAV) platforms or from proximal sensors attached to ground-based vehicles used for regular management. Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations. Therefore, the objective of this study was to investigate if regression kriging, as a geostatistical data fusion approach, can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate. Regression kriging might be suitable because we have a sparse data set (ultrasound) and an exhaustive data set (UAV) and both data sets have favorable properties for geostatistical analysis. To confirm this, we conducted four missions in two different fields in total, where we collected UAV imagery and ultrasonic data alongside. From the overlapping UAV images, surface models and ortho-images were generated with photogrammetric processing. The maps generated by regression kriging were of much higher detail than the smooth maps generated by ordinary kriging, because regression kriging ensures that for each prediction point information from the UAV, imagery is given. The relationship with crop height, fresh biomass and, to a lesser extent, with crop yield, was stronger using CHMs generated by regression kriging than by ordinary kriging. The use of UAV data from the prior mission was also of benefit and could improve map accuracy and quality. Thus, regression kriging is a flexible approach for the integration of UAV imagery with ground-based sensor data, with benefits for precision agriculture-oriented farmers and agricultural service providers.