Institut für Agrartechnik

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/19

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Disc mower versus bar mower: Evaluation of the direct effects of two common mowing techniques on the grassland arthropod fauna
(2025) von Berg, Lea; Frank, Jonas; Betz, Oliver; Steidle, Johannes L. M.; Böttinger, Stefan; Sann, Manuela; von Berg, Lea; Evolutionary Biology of Invertebrates, Institute for Evolution and Ecology, University of Tübingen, Tübingen, Germany; Frank, Jonas; Fundamentals of Agricultural Engineering, Institute for Agricultural Engineering, University of Hohenheim, Stuttgart, Germany; Betz, Oliver; Evolutionary Biology of Invertebrates, Institute for Evolution and Ecology, University of Tübingen, Tübingen, Germany; Steidle, Johannes L. M.; Chemical Ecology, Institute for Biology, University of Hohenheim, Stuttgart, Germany; Böttinger, Stefan; Fundamentals of Agricultural Engineering, Institute for Agricultural Engineering, University of Hohenheim, Stuttgart, Germany; Sann, Manuela; Chemical Ecology, Institute for Biology, University of Hohenheim, Stuttgart, Germany
1. In Central Europe, species‐rich grasslands are threatened by intensive agriculture with frequent mowing, contributing to the reduction of arthropods such as insects and spiders. However, comprehensive and standardised studies on the direct effects of the two most agriculturally relevant mowing techniques, e.g., double‐blade bar mower versus disc mower, are lacking. 2. In a 2‐year experiment, we have investigated the direct effect of mowing on eight abundant arthropod groups in grassland, covering two seasonal mowing events in both years, using a randomised block design. We compared (a) an unmown control, (b) a double‐blade bar mower and (c) a disc mower. 3. For most of the taxonomic groups studied, a significantly lower number of individuals was found in the experimental plots immediately after mowing, regardless of the mowing technique, compared to an unmown control. This was not the case for Orthoptera and Coleoptera, which did not show a significant reduction in the number of individuals for both mowing techniques (Orthoptera) or only for the double‐blade bar mower (Coleoptera). 4. Between both mowing techniques, no significant differences were found for all taxonomic groups investigated. 5. Synthesis and applications: Our findings suggest that mowing in general has a negative impact on abundant arthropod groups in grassland, regardless of the method used. Tractor‐driven double‐blade bar mowers do not seem to be a truly insect‐friendly alternative to a conventional disc mower. Other factors such as cutting height and mowing regimes should be seriously considered to protect spiders and insects from the negative effects of mowing. In addition, we strongly recommend the maintenance of unmown refugia. Insects and spiders that are spared by mowing can take refuge in these unmown areas to avoid subsequent harvesting and thermally unfavourable conditions that arise on mown areas. Further, unmown refugia are basic habitat structures for a subsequent recolonisation of mown areas once the flora has recovered.
Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements
(2024) Han, Leng; Wang, Zhichong; He, Miao; He, Xiongkui; Han, Leng; College of Science, China Agricultural University, Beijing, China; Wang, Zhichong; Tropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, Germany; He, Miao; College of Science, China Agricultural University, Beijing, China; He, Xiongkui; College of Science, China Agricultural University, Beijing, China
The nonuniform distribution of fruit tree canopies in space poses a challenge for precision management. In recent years, with the development of Structure from Motion (SFM) technology, unmanned aerial vehicle (UAV) remote sensing has been widely used to measure canopy features in orchards to balance efficiency and accuracy. A pipeline of canopy volume measurement based on UAV remote sensing was developed, in which RGB and digital surface model (DSM) orthophotos were constructed from captured RGB images, and then the canopy was segmented using U-Net, OTSU, and RANSAC methods, and the volume was calculated. The accuracy of the segmentation and the canopy volume measurement were compared. The results show that the U-Net trained with RGB and DSM achieves the best accuracy in the segmentation task, with mean intersection of concatenation (MIoU) of 84.75% and mean pixel accuracy (MPA) of 92.58%. However, in the canopy volume estimation task, the U-Net trained with DSM only achieved the best accuracy with Root mean square error (RMSE) of 0.410 m 3 , relative root mean square error (rRMSE) of 6.40%, and mean absolute percentage error (MAPE) of 4.74%. The deep learning-based segmentation method achieved higher accuracy in both the segmentation task and the canopy volume measurement task. For canopy volumes up to 7.50 m 3 , OTSU and RANSAC achieve an RMSE of 0.521 m 3 and 0.580 m 3 , respectively. Therefore, in the case of manually labeled datasets, the use of U-Net to segment the canopy region can achieve higher accuracy of canopy volume measurement. If it is difficult to cover the cost of data labeling, ground segmentation using partitioned OTSU can yield more accurate canopy volumes than RANSAC.
Food informatics - Review of the current state-of-the-art, revised definition, and classification into the research landscape
(2021) Krupitzer, Christian; Stein, Anthony
Background: The increasing population of humans, changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the Internet of Things (IoT) and Artificial Intelligence (AI) technology, including Machine Learning and data analytics, might help to account for these challenges. Scope and Approach: Several research perspectives, among them Precision Agriculture, Industrial IoT, Internet of Food, or Smart Health, already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept is Food Informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of Food Informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangent to the food production process and delineate it from other, existing research streams in the domain. Key Findings and Conclusions: Many different concepts related to the digitalization in food science overlap. Further, Food Informatics is vaguely defined. In this paper, we provide a clear definition of Food Informatics and delineate it from related concepts. We corroborate our new perspective on Food Informatics by presenting several case studies about how it can support the food production as well as the intermediate steps until its consumption, and further describe its integration with related concepts.

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