Fakultätsübergreifend / Sonstige Einrichtung

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Assessing functional properties of diet protein hydrolysate and oil from fish waste on canine immune parameters, cardiac biomarkers, and fecal microbiota
(2024) Cabrita, Ana R. J.; Barroso, Carolina; Fontes-Sousa, Ana Patrícia; Correia, Alexandra; Teixeira, Luzia; Maia, Margarida R. G.; Vilanova, Manuel; Yergaliyev, Timur; Camarinha-Silva, Amélia; Fonseca, António J. M.
Locally produced fish hydrolysate and oil from the agrifood sector comprises a sustainable solution both to the problem of fish waste disposal and to the petfood sector with potential benefits for the animal’s health. This study evaluated the effects of the dietary replacement of mainly imported shrimp hydrolysate (5%) and salmon oil (3%; control diet) with locally produced fish hydrolysate (5%) and oil (3.2%) obtained from fish waste (experimental diet) on systemic inflammation markers, adipokines levels, cardiac function and fecal microbiota of adult dogs. Samples and measurements were taken from a feeding trial conducted according to a crossover design with two diets (control and experimental diets), six adult Beagle dogs per diet and two periods of 6 weeks each. The experimental diet, with higher docosahexaenoic (DHA) and eicosapentaenoic (EPA) acids contents, decreased plasmatic triglycerides and the activity of angiotensin converting enzyme, also tending to decrease total cholesterol. No effects of diet were observed on serum levels of the pro-inflammatory cytokines interleukin (IL)-1β, IL-8, and IL-12/IL-23 p40, and of the serum levels of the anti-inflammatory adipokine adiponectin. Blood pressure, heart rate and echocardiographic measurements were similar between diets with the only exception of left atrial to aorta diameter ratio that was higher in dogs fed the experimental diet, but without clinical relevance. Diet did not significantly affect fecal immunoglobulin A concentration. Regarding fecal microbiome, Megasphaera was the most abundant genus, followed by Bifidobacterium , Fusobacterium , and Prevotella , being the relative abundances of Fusobacterium and Ileibacterium genera positively affected by the experimental diet. Overall, results from the performed short term trial suggest that shrimp hydrolysate and salmon oil can be replaced by protein hydrolysate and oil from fish by-products without affecting systemic inflammatory markers, cardiac structure and function, but potentially benefiting bacterial genera associated with healthy microbiome. Considering the high DHA and EPA contents and the antioxidant properties of fish oil and hydrolysate, it would be worthwhile in the future to assess their long-term effects on inflammatory markers and their role in spontaneous canine cardiac diseases and to perform metabolomic and metagenomics analysis to elucidate the relevance of microbiota changes in the gut.
Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection
(2025) Allmendinger, Alicia; Saltık, Ahmet Oğuz; Peteinatos, Gerassimos G.; Stein, Anthony; Gerhards, Roland
Spot spraying represents an efficient and sustainable method for reducing herbicide use in agriculture. Reliable differentiation between crops and weeds, including species-level classification, is essential for real-time application. This study compares state-of-the-art object detection models-YOLOv8, YOLOv9, YOLOv10, and RT-DETR-using 5611 images from 16 plant species. Two datasets were created, dataset 1 with training all 16 species individually and dataset 2 with grouping weeds into monocotyledonous weeds, dicotyledonous weeds, and three chosen crops. Results indicate that all models perform similarly, but YOLOv9s and YOLOv9e, exhibit strong recall (66.58 % and 72.36 %) and mAP50 (73.52 % and 79.86 %), and mAP50-95 (43.82 % and 47.00 %) in dataset 2. RT-DETR-l, excels in precision reaching 82.44 % (dataset 1) and 81.46 % (dataset 2) making it ideal for minimizing false positives. In dataset 2, YOLOv9c attains a precision of 84.76% for dicots and 78.22% recall for Zea mays L.. Inference times highlight smaller YOLO models (YOLOv8n, YOLOv9t, and YOLOv10n) as the fastest, reaching 7.64 ms (dataset 1) on an NVIDIA GeForce RTX 4090 GPU, with CPU inference times increasing significantly. These findings emphasize the trade-off between model size, accuracy, and hardware suitability for real-time agricultural applications.
Coping and social cohesion mechanisms in addressing climate change and land degradation in Ghana
(2025) Amankwah, Harry Quaye; Ndah, Hycenth Tim; Schuler, Johannes; Abdulai, Alhassan Lansah; Knierim, Andrea
The West Africa sub-region is faced with major interlinked challenges in ensuring sustainable livelihoods in the context of climate change and land degradation. To ensure sustainable food production and resource use, agriculture needs to be resilient through the application of responsive adaptation and coping strategies. While many studies have explored coping and adaptation strategies employed by farmers, little attention has been paid to the farmers’ indigenous practices and the role of social cohesion mechanisms. Using the sustainable livelihood framework, this study addressed this gap by exploring coping strategies and social cohesion mechanisms used by smallholder farmers in northern Ghana. It made use of a mixed-method approach, including a household survey, focus group discussions, expert interviews, field observations, and key informant interviews. Data was collected from 60 households in 6 communities across 3 districts in the study region. The results showed that social assets such as membership of self-help groups were the most important source of coping, particularly for the most vulnerable households. Such membership enabled farmers to secure micro-loans and receive aid from fellow members during extreme climate events such as floods. Farmers’ tacit knowledge emerged as pivotal in coping with climate change and enhancing soil fertility, encompassing traditional weather forecasting, the making of bio-pesticides, and sustainable land management (SLM) practices such as ridge and bund creation as well as intercropping. Key coping practices reported by the study participants included reduction of food consumption, off-farm jobs, selling livestock, charcoal making and reliance on remittances. The results further revealed that social cohesion mechanisms or collective action play a key role in helping farmers cope and adapt to climate change while improving soil fertility. Social cohesion is mainly reflected in two different structures depending on gender. While diverse challenges of innovation adoption exist, socio-cultural barriers differ by gender. The study recommends the integration of farmers throughout the innovation development process and proposes the need for a concerted effort to strengthen land tenure security policies, ensuring equitable access to farmlands for all genders.
Cow’s microbiome from antepartum to postpartum: a long-term study covering two physiological challenges
(2022) Tröscher-Mußotter, Johanna; Deusch, Simon; Borda-Molina, Daniel; Frahm, Jana; Dänicke, Sven; Camarinha-Silva, Amélia; Huber, Korinna; Seifert, Jana
Little is known about the interplay between the ruminant microbiome and the host during challenging events. This long-term study investigated the ruminal and duodenal microbiome and metabolites during calving as an individual challenge and a lipopolysaccharide-induced systemic inflammation as a standardized challenge. Strong inter- and intra-individual microbiome changes were noted during the entire trial period of 168 days and between the 12 sampling time points. Bifidobacterium increased significantly at 3 days after calving. Both challenges increased the intestinal abundance of fiber-associated taxa, e.g., Butyrivibrio and unclassified Ruminococcaceae. NMR analyses of rumen and duodenum samples identified up to 60 metabolites out of which fatty and amino acids, amines, and urea varied in concentrations triggered by the two challenges. Correlation analyses between these parameters indicated a close connection and dependency of the microbiome with its host. It turns out that the combination of phylogenetic with metabolite information supports the understanding of the true scenario in the forestomach system. The individual stages of the production cycle in dairy cows reveal specific criteria for the interaction pattern between microbial functions and host responses.
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, Karl
Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
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.
Functionality of the Na+-translocating NADH:quinone oxidoreductase and quinol:fumarate reductase from Prevotella bryantii inferred from homology modeling
(2024) Hau, Jann-Louis; Schleicher, Lena; Herdan, Sebastian; Simon, Jörg; Seifert, Jana; Fritz, Günter; Steuber, Julia
Members of the family Prevotellaceae are Gram-negative, obligate anaerobic bacteria found in animal and human microbiota. In Prevotella bryantii , the Na + -translocating NADH:quinone oxidoreductase (NQR) and quinol:fumarate reductase (QFR) interact using menaquinone as electron carrier, catalyzing NADH:fumarate oxidoreduction. P. bryantii NQR establishes a sodium-motive force, whereas P. bryantii QFR does not contribute to membrane energization. To elucidate the possible mode of function, we present 3D structural models of NQR and QFR from P. bryantii to predict cofactor-binding sites, electron transfer routes and interaction with substrates. Molecular docking reveals the proposed mode of menaquinone binding to the quinone site of subunit NqrB of P. bryantii NQR. A comparison of the 3D model of P. bryantii QFR with experimentally determined structures suggests alternative pathways for transmembrane proton transport in this type of QFR . Our findings are relevant for NADH-dependent succinate formation in anaerobic bacteria which operate both NQR and QFR.
Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life
(2025) Senge, Julia Marie; Kaltenecker, Florian; Krupitzer, Christian
Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life.

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