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Westerdijk, K
Asgedom, H
Almeida, S.L
Auer, W
Abney, M
Alexandroff, V
Wang, Y
Wörlein, N
Qiao, S
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Authors
Yang, X
Sun, C
Qian, J
Ji, Z
Qiao, S
Chen, M
Zhao, C
Li, M
Kempenaar, C
van Evert, F
Been, T
Kocks, C
Westerdijk, K
Nysten, S
Roland, L
Lidauer, L
Sattlecker, G
Kickinger, F
Auer, W
Sturm, V
Efrosinin, D
Drillich, M
Iwersen, M
Berger, A
Iwersen, M
Reiter, S
Schweinzer, V
Kickinger, F
Öhlschuster, M
Lidauer, L
Auer, W
Drillich, M
Berger, A
Krieger, S
Oczak, M
Lidauer, L
Kickinger, F
Öhlschuster, M
Auer, W
Drillich, M
Iwersen, M
Berger, A
Schweinzer, V
Lidauer, L
Kickinger, F
Öhlschuster, M
Auer, W
Drillich, M
Iwersen, M
Berger, A
Claussen, J
Wörlein, N
Uhlmann, N
Gerth, S
Wang, Y
Balmos, A
Krogmeier, J
Buckmaster, D
Krogmeier, J
Buckmaster, D
Ault, A
Wang, Y
Zhang, Y
Layton, A
Noel, S
Balmos, A
Paz Kagan, T
Alexandroff, V
Ungar, E.D
Rossi, C
Almeida, S.L
Sysskind, M.N
Moreno, L.A
Felipe dos Santos, A
Lacerda, L
Vellidis, G
Pilcon, C
Orlando Costa Barboza, T
Vellidis, G
Abney, M
Burlai, T
Fountain, J
Kemerait, R.C
Kukal, S
Lacerda, L
Maktabi, S
Peduzzi, A
Pilcon, C
Sysskind, M
Asgedom, H
Hehar, G
Willness, C
Anderson, W
Duddu, H
Mooleki, P
Schoenau, J
Khakbazan, M
Lemke, R
Derdall, E
Shang, J
Liu, K
Sulik, J
Karppinen, E
Mbakwe, I
Topics
Precision Crop Protection
Decision Support Systems in Precision Agriculture
Precision Dairy and Livestock Management
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Big Data, Data Mining and Deep Learning
Profitability and Success Stories in Precision Agriculture
Site-Specific Pasture Management
Artificial Intelligence (AI) in Agriculture
Decision Support Systems
Site-Specific Nutrient, Lime and Seed Management
Type
Poster
Oral
Year
2012
2016
2018
2024
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Filter results13 paper(s) found.

1. Modeling and Decision Support System for Precision Cucumber Protection in Greenhouses

The plant disease... X. Yang, C. Sun, J. Qian, Z. Ji, S. Qiao, M. Chen, C. Zhao, M. Li

2. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the Netherlands

Precision farming (PF) will contribute to more sustainable agriculture and the global challenge of producing ‘More with less’. It is based on the farm management concept of observing, measuring and responding to inter- and intra-field variability in crops. Computers enabled the use of Farm Management Information Systems (FMIS) and farm and field specific Decision Support Systems (DSS) since mid-1980s. GIS and GNSS allowed since ca. 2000 geo-referencing of data and controlled traffic... C. Kempenaar, F. Van evert, T. Been, C. Kocks, K. Westerdijk, S. Nysten

3. A Pilot Study on Monitoring Drinking Behavior in Bucket Fed Dairy Calves Using an Ear-Attached Tri-Axial Accelerometer

Accelerometers support the farmer with collecting information about animal behavior and thus allow a reduction in visual observation time. The milk intake of calves fed by teat-buckets has not been monitored automatically on commercial farms so far, although it is crucial for the calves’ development. This pilot study was based on bucket-fed dairy calves and intended (1) to evaluate the technical feasibility of using an ear-attached accelerometer (SMARTBOW, Smartbow GmbH, Weibern, Austria)... L. Roland, L. Lidauer, G. Sattlecker, F. Kickinger, W. Auer, V. Sturm, D. Efrosinin, M. Drillich, M. Iwersen, A. Berger

4. Evaluation of an Ear Tag Based Accelerometer for Monitoring Rumination Time, Chewing Cycles and Rumination Bouts in Dairy Cows

The objective of this study was to evaluate the ear tag based accelerometer SMARTBOW (Smartbow, Weibern, Austria) for detecting rumination time, chewing cycles and rumination bouts in dairy cows. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. Additionally, the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings was tested. Ten Simmental cows... M. Iwersen, S. Reiter, V. Schweinzer, F. Kickinger, M. Öhlschuster, L. Lidauer, W. Auer, M. Drillich, A. Berger

5. Ear-Attached Accelerometer as an On-Farm Device to Predict the Onset of Calving in Dairy Cows

The objective of this study on an ear-attached accelerometer in dairy cows was (1) to determine activity, rumination and lying time of the dams prior to calving, and include group level of measured variables (2) use the data to develop an algorithm to predict calving and (3) to test the performance of this algorithm. Video observations (24h/d) were used as reference for these events. Four weeks before expected calving, an ear-tag integrated tri-axial accelerometer (SMARTBOW system) was attached... S. Krieger, M. Oczak, L. Lidauer, F. Kickinger, M. Öhlschuster, W. Auer, M. Drillich, M. Iwersen, A. Berger

6. Evaluation of the Ear-Tag Sensor System SMARTBOW for Detecting Estrus Events in Indoor Housed Dairy Cows

Livestock farming technologies have a tremendous potential to improve and support farmers in herd management decisions, in particular in reproductive management. Nowadays, estrus detection in cows is challenging and many detection tools are available. The company Smartbow (Weibern, Austria) developed a novel ear-tag sensor, which consists of a 3D-accelerometer that records head and ear movements of cows as basis for algorithm development and further analyses. Estrus detection by the SMARTBOW system... V. Schweinzer, L. Lidauer, F. Kickinger, M. Öhlschuster, W. Auer, M. Drillich, M. Iwersen, A. Berger

7. Quantification of Seed Performance: Non-Invasive Determination of Internal Traits Using Computed Tomography

The application of the 3D mean-shift filter to 3D Computed Tomography Data enables the segmentation of internal traits. Specifically in maize seeds this approach gives the opportunity to separate the internal structure, for example the volume of the embryo, the cavities and the low and high dense parts of the starch body. To evaluate the mean-shift filter, the results were compared to the usage of a median-smoothing filter. To show the relevance of the mean-shift extended image pipeline an automatic... J. Claussen, N. Wörlein, N. Uhlmann, S. Gerth

8. Data-Driven Agricultural Machinery Activity Anomaly Detection and Classification

In modern agriculture, machinery has become the one of the necessities in providing safe, effective and economical farming operations and logistics. In a typical farming operation, different machines perform different tasks, and sometimes are used together for collaborative work. In such cases, different machines are associated with representative activity patterns, for example, in a harvest scenario, combines move through a field following regular swaths while grain carts follow irregular paths... Y. Wang, A. Balmos, J. Krogmeier, D. Buckmaster

9. Use Cases for Real Time Data in Agriculture

Agricultural data of many types (yield, weather, soil moisture, field operations, topography, etc.) comes in varied geospatial aggregation levels and time increments. For much of this data, consumption and utilization is not time sensitive. For other data elements, time is of the essence. We hypothesize that better quality data (for those later analyses) will also follow from real-time presentation and application of data for it is during the time that data is being collected that errors can be... J. Krogmeier, D. Buckmaster, A. Ault, Y. Wang, Y. Zhang, A. Layton, S. Noel, A. Balmos

10. Detection of Goat Herding Impact on Vegetation Cover Change Using Multi-season, Multi-herd Tracking and Satellite Imagery

The frequency and severity of Mediterranean forest fires are expected to worsen as climate change progresses, heightening the need to evaluate understory fuel management strategies as rigorously as possible. Prescribed small-ruminant foraging is considered a sustainable, cost-effective strategy, but demonstrating a link between animal presence and vegetation change is challenging. This study tested whether the effect of small-ruminant herd presence in Mediterranean woodlands can be detected by... T. Paz kagan, V. Alexandroff, E.D. Ungar

11. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic Parameters

The environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States in... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe dos santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando costa barboza

12. Decision Support Tools for Developing Aflatoxin Risk Maps in Peanut Fields

Aspergillus flavus and Aspergillus parasiticus hereafter referred to jointly as A. flavus, are soil fungi that infect and contaminate preharvest and postharvest peanuts with the carcinogenic secondary metabolite aflatoxin. A. flavus can cause extensive economic losses to peanut growers and shellers by contaminating peanut kernels with aflatoxins. In the southeastern U.S., contamination from aflatoxin continues to be a major threat to the peanut industry and... G. Vellidis, M. Abney, T. Burlai, J. Fountain, R.C. Kemerait, S. Kukal, L. Lacerda, S. Maktabi, A. Peduzzi, C. Pilcon, M. Sysskind

13. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management Zones

Investment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe