Proceedings
Authors
| Filter results18 paper(s) found. |
|---|
1. Towards a Multi-Source Record Keeping System for Agricultural Product TraceabilityAgricultural production record keeping is the basis of traceability system. To resolve the problem including single method of information acquisition, weak ability of real-time monitoring and low credibility of history information in agricultural production process, the... C. Sun, Z. Ji, J. Qian, M. Li, L. Zhao, W. Li, C. Zhou, X. Du, J. Xie, T. Wu, L. Qu, L. Hao, X. Yang |
2. Modeling and Decision Support System for Precision Cucumber Protection in GreenhousesThe plant disease... X. Yang, C. Sun, J. Qian, Z. Ji, S. Qiao, M. Chen, C. Zhao, M. Li |
3. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial ImagesPotato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud |
4. BrainWeed - Teach-In System for Adaptive High Speed Crop / Weed Classification and TargetingConducting inter row mechanical weeding requires the precise location of each individual crop plant is known. One technique is to record the global position of each seed when sown using RTK-GPS systems. Another... R.N. J�??�?�¸rgensen, H.S. Midtiby, T.M. Giselsson |
5. Traceability And Management Information System Of Agricultural Product Quality Safety In ChinaAgricultural product quality safety is the hot topic in the world. From the technical view, the agricultural production management and traceability are the key measurement for insuring the quality safety. From 2005 until now, we have been investigating... X. Yang, M. Li, C. Sun, J. Qian, Z. Ji |
6. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
7. Use of a Cropping System Model for Soil-specific Optimization of Limited WaterIn the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irrigation... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen |
8. Grazing System and Solar Fences, Innovation and Opportunity in Rangeland of Developing CountriesThe future of the development and management of pasture resources depends on increasing the use of scientific innovations. In some countries rangeland livestock production majority relies on natural ecological processes of plant and animal production, despite the progress in all of the infrastructure, rangeland management have a little growth and base on traditional ranching management, grazing livestock is based on a free grazing system. In this study grazing system was applied and electric fence... H. Arzani, E. Alizadeh |
9. A Pilot Study on Monitoring Drinking Behavior in Bucket Fed Dairy Calves Using an Ear-Attached Tri-Axial AccelerometerAccelerometers 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 |
10. Evaluation of an Ear Tag Based Accelerometer for Monitoring Rumination Time, Chewing Cycles and Rumination Bouts in Dairy CowsThe 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 |
11. Ear-Attached Accelerometer as an On-Farm Device to Predict the Onset of Calving in Dairy CowsThe 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 |
12. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem ModelCorn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by the... T. Nigon, D. Mulla, C. Yang |
13. Delineation of Site-Specific Nutrient Management Zones to Optimize Rice Production Using Proximal Soil Sensing and Multispectral ImagingEvaluating nutrient uptake and site-specific nutrient management zones in rice in Costa Rica from plant tissue and soil sampling is expensive because of the time and labor involved. In this project, a range of measurement techniques were implemented at different vintage points (soil, plant and UAVs) in order to generate and compare nutrient management information. More precisely, delineation of site-specific nutrient management zones were determined using 1) georeferenced soil/tissue... J.E. Villalobos, J.S. Perret, K. Abdalla, C.L. Fuentes, J.C. Rodriguez, W. Novais |
14. Evaluation of the Ear-Tag Sensor System SMARTBOW for Detecting Estrus Events in Indoor Housed Dairy CowsLivestock 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 |
15. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in MinnesotaCompact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen treatments... A. Laacouri, T. Nigon, D. Mulla, C. Yang |
16. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer ResponseImproving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three growing... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer |
17. Assessing Crop Yield and Profitability with Site-specific Seed Rate Management in Corn and Soybean Cropping SystemsIntegrating the information about soil and topographic properties for variable rate seeding is a prerequisite for improved crop production and thus profit. However, limited studies have explored the geospatial and machine learning approaches to understand factors influencing crop yield and profit under site-specific seed rate management. The objectives of this study were to: a) observe the effect of variable seeding rate based on soil and topographic properties on soybean and corn grain yield,... J. Neupane, N. Joshi, J.P. Fulton, S. Khanal, A. B k, B. Bhattarai |
18. Using Machine Vision to Build Field Maps of Forage Quality and the Need for Agriculture-specific Machine Vision NetworksMachine vision systems have truly come of age over the past decade. These networks are relatively simple to implement with systems such as YOLOv5 or the more recent YOLOv8. They are also relatively easy and computationally cheap to retrain to a custom data set, allowing for customization of these networks to new object detection and classification tasks. With this ease, it is no surprise that we are seeing an explosion of these networks and their application through all aspects of agriculture.... P. Nugent, J. Neupane |