Browsing by Person "Griepentrog, Hans W."

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AI-based planting and monitoring of cabbage with a robotic platform
(2024) Lüling, Nils; Griepentrog, Hans W.
Labour shortages, price pressure and changes in legislation are just a few of the drivers of automation and digitalization in field vegetable cultivation. Due to its high-value crops and its high demands on crop maintenance, field vegetable cultivation is the ideal working area for agricultural robotics. However, the versatile and rapid establishment of agricultural robotics systems has so far failed due to the limited adaptivity to the complex working environment under outdoor conditions, the process chain and the applications that an agricultural robot has to carry out in a field. Only through the developing possibilities of using cameras and artificial intelligence can complex automated applications be implemented. The overall aim of this cumulative dissertation was the development and analysis of systems for AI-based crop establishment and crop maintenance of white cabbage with a robotic platform. Three aspects were analysed: (1) Design, prototyping and evaluation of a planting unit for an autonomous planting process of cabbage with a robotic platform. By using AI-based image classification, a camera at the end of the planting unit was used to evaluate the planting quality and dynamically adjust individual planting parameters. (2) Development of a camera-based vegetation monitoring system for determining the fruit volume and leaf area of white cabbage across several growth stages. (3) Analysis of a method for unsupervised image translation for automated exposure adjustment. By reducing the exposure variation, a lower implementation effort and a higher robustness of the detection and segmentation of white cabbage are aimed for. As part of the autonomous crop establishment, a planting unit was developed and constructed that can carry out an automated crop stand establishment process using a robot platform. The analysis of the quality of the planting process showed a comparable planting performance and planting accuracy to conventional systems of automated field vegetable planting. During the development of the planting unit, the focus was placed on an adaptive design of the unit so that machine parameters can be dynamically adjusted during the planting process. It was possible to reduce the energy requirement of the overall system by dynamically opening and closing the planting furrow during the planting process in order to minimize the draft force. It also creates the basis for an autonomous planting process. Using an attached camera and an AI for image classification, the planting quality can also be recorded and planting parameters such as the planting depth and furrow width can then be adjusted in order to influence the plant placement. At the same time, the AI-based image classification can also be used to control the planting process itself. If the planting tape tears or the separation is blocked, no seedlings are planted. The AI recognizes this and can instruct the robot to suspend the planting process. For automated crop monitoring, the camera, in cooperation with a neural network for instance segmentation, offers the possibility of a contact-free and high-resolution recording of plant parameters. Using instance segmentation of the cabbage head, the cabbage plant and the individual cabbage leaves, as well as a depth image generation using structure-from-motion, it was possible to determine plant parameters such as the absolute leaf area, the number of leaves or the fruit volume of the cabbage head across several growth stages. This offers farmers new opportunities in crop management, which can be tailored even more specifically to individual plants using the information collected. As many possibilities as the use of cameras in combination with neural network-based image analysis offers, there are still some challenges. One of the fundamental challenges lies in the provision and annotation of image data to ensure robust image analysis. The more complex the use case, the more varying images the data set must contain in order to provide the neural network with a basis of information with which it can learn the necessary features. To reduce the complexity of the use case of detecting and segmenting cabbage plants, an AI-based image translation was used to standardize the exposure variations. No annotation is required to train the AI-based image translation, which is trained unsupervised. By standardizing the exposure, the complexity of the images can be reduced, which means that fewer images need to be annotated for a robust use of instance segmentation. This method was also tested for varying growth stages and varieties.
Development and experimental validation of an agricultural robotic platform with high traction and low compaction
(2023) Reiser, David; Sharipov, Galibjon M.; Hubel, Gero; Nannen, Volker; Griepentrog, Hans W.
Some researchers expect that future agriculture will be automated by swarms of small machines. However, small and light robots have some disadvantages. They have problems generating interaction forces high enough to modify the environment (lift a stone, cultivate the soil, or transport high loads). Additionally, they have limited range and terrain mobility. One option to change this paradigm is to use spikes instead of wheels, which enter the soil to create traction. This allows high interaction forces with the soil, and the process is not limited by the weight of the vehicle. We designed a prototype for mechanical soil cultivation and weeding in agricultural fields and evaluated its efficiency. A static and dynamic test was performed to compare the energy input of the electrical motor with precise measurements of the forces on the attached tool. The results indicate that the prototype can create interaction forces of up to 2082 N with a robot weight of 90 kg. A net traction ratio of 2.31 was reached. The dynamic performance experiment generated pull forces of up to 1335 N for a sustained net traction ratio of 1.48. The overall energy efficiency ratio for the machine reached values of up to 0.54 based on the created draft force and the measured input energy consumption.
Längsverteilung von Sämaschinen und ihre Wirkung auf Standfläche und Ertrag bei Raps
(1995) Griepentrog, Hans W.
Die Ablagequalität von Sämaschinen beeinflusst den Feldaufgang und die Entwicklung von Pflanzen und damit auch den Flächenertrag. Um den Einfluss der Längsverteilung auf den Flächenertrag darzustellen, wurden Daten der maschinenbedingten Standflächenverhältnisse und des Einzelpflanzenertrages von Rapspflanzen über eine Modellbildung miteinander verrechnet. Die Standflächen der Einzelpflanzen wurden mittels Polygonzerlegung bestimmt, während die Einzelpflanzenerträge aus der Pflanzenbauforschung übernommen wurden. Gleichmäßige Längsverteilungen ergaben gleichmäßige Standflächenverteilungen und höhere Flächenerträge. Der Einfluss des Reihenabstandes nimmt mit zunehmender Gleichmäßigkeit der Längsverteilung ab.
Position accuracy assessment of a uav-mounted sequoia+ multispectral camera using a robotic total station
(2022) Paraforos, Dimitrios S.; Sharipov, Galibjon M.; Heiß, Andreas; Griepentrog, Hans W.
Remote sensing data in agriculture that are originating from unmanned aerial vehicles (UAV)-mounted multispectral cameras offer substantial information in assessing crop status, as well as in developing prescription maps for site-specific variable rate applications. The position accuracy of the multispectral imagery plays an important role in the quality of the final prescription maps and how well the latter correspond to the specific spatial characteristics. Although software products and developed algorithms are important in offering position corrections, they are time- and cost-intensive. The paper presents a methodology to assess the accuracy of the imagery obtained by using a mounted target prism on the UAV, which is tracked by a ground-based total station. A Parrot Sequoia+ multispectral camera was used that is widely utilized in agriculture-related remote sensing applications. Two sets of experiments were performed following routes that go along the north–south and east–west axes, while the cross-track error was calculated for all three planes, but also three-dimensional (3D) space. From the results, it was indicated that the camera’s D-GNSS receiver can offer imagery with a 3D position accuracy of up to 3.79 m, while the accuracy in the horizontal plane is higher compared to the vertical ones.
Stimulating awareness of precision farming through gamification: The farming simulator case
(2024) Pavlenko, Tetiana; Argyropoulos, Dimitrios; Arnoult, Matthieu; Engel, Thomas; Gadanakis, Yiorgos; Griepentrog, Hans W.; Kambuta, Jacob; Latherow, Tamisan; Murdoch, Alistair J.; Tranter, Richard; Paraforos, Dimitrios S.
Precision Farming (PF) provides different solutions to assist the decision-making process on farms. Current PF technologies such as variable rate site-specific applications can bring financial benefits to farmers as well as environmental advantages. Increasing scientific research and an expanding number of PF products are supporting a growing interest in PF applications. However, the actual implementation of these technologies on farms in many cases remains low. Therefore, there is a need to disseminate and transfer knowledge about the positive aspects of PF. One of the ways to facilitate the adoption process of PF technologies is education and training among farmers and other interested stakeholders. This paper presents a case study using the computer game Farming Simulator as an educational tool for raising awareness about the topic in an engaging and enjoyable way. Two distinct downloadable content (DLC) versions were developed and implemented in the versions 2019 and 2022 of the game, respectively, each with a range of PF functionalities (automatic steering, variable rate applications, yield mapping among others). The PF DLCs have received positive feedback from students and scientists but also the general public. The growing number of downloads (3,661,069 in total for both DLC versions as of 15th November 2023) demonstrates the effectiveness of computer games as an educational tool to educate and inform stakeholders (farmers, scientists, students, and the general public) about agricultural challenges and the potential of PF as a solution.
Zur Bewertung der Flächenverteilung von Saatgut
(1999) Griepentrog, Hans W.
Die Ablagequalität von Sämaschinen beeinflusst den Feldaufgang und die Pflanzenentwicklung und damit auch den Flächenertrag. Zur Bewertung der Ablagequalität ist neben der Tiefenablage die horizontale Flächenverteilung zu berücksichtigen. Eine Optimierung der Pflanzenabstände erhöht Feldaufgang und Ertrag, indem Konkurrenzeffekte um die Wachstumsfaktoren Licht, Wasser und Nährstoffe minimiert werden. Die Flächenverteilung von Saatgut ist abhängig von der Längsverteilungsqualität der Sämaschine, vom Reihenabstand und von der Aussaatmenge als nichttechnischem Parameter. Diese drei Parameter bestimmen die Qualität der Flächenverteilung und damit die für jede Pflanze zur Verfügung stehende Standfläche bzw. den Entwicklungsraum. Es wird ein Verfahren zur Darstellung und Bewertung von Flächenverteilungen beschrieben, das über eine Polygonzerlegung Einzelstandflächen definiert und die Parameter Längsverteilung, Reihenabstand und Aussaatmenge berücksichtigt. Ergebnisse von Flächenverteilungen für Raps, Weizen und Mais werden gezeigt.