Proceedings
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| Filter results12 paper(s) found. |
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1. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
2. The Device of Air-assisted Side Deep Precision Fertilization for Rice TransplanterRice is the most important crop in China, which has the largest plant area. Fertilization is an important process of rice production, which directly affects the yield of crops, reasonable and effective use of chemical fertilizer can improve the yield of crops. At present, the mechanization level of rice fertilization is very low in China, and the artificial fertilization requires a large amount of fertilizer which caused the uneven distribution. The rice side deep fertilizing is an ideal way of... C. Zhao, G. Wu, Z. Meng, W. Fu, L. Li, X. Wei |
3. Development of Land Leveling Equipment Based on GNSSAn attitude adjustable land leveling equipment was designed. The reference elevation of the land to be leveled was generated based on the topographic data which was acquired by the RTK-GNSS technology. The blade lifting mechanism was controlled by comparing the reference elevation and the real-time blade’s elevation and attitude data which was obtained by the dual antenna GNSS receiver and as a result the land leveling operation was implemented. A new algorithm using the electro-hydraulic... W. Fu, G. Wu, H. Bao, X. Wei, Z. Meng |
4. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
5. Harness the Power of the Internet to Improve YieldIt’s rare to find a fertile farm or ranch that has complete cellular coverage across the entirety of its property. Because networking options like Wi-Fi are limited by restricted infrastructure in these areas, maintaining a reliable flow of connectivity is difficult. Yet, even if consistent cellular coverage is available, it’s frequently cost prohibitive for farm monitoring. Similarly, alternate wireless devices that require batteries aren’t practical because of high maintenance... M. Finegan, D. Wallace |
6. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
7. The Evaluation of NDVI Response Index Consistency Using Proximal Sensors, UAV and SatellitesThe Response Index NDVI (RINDVI) is described as the response of crops to additional nitrogen (N) fertilizer. It is calculated by dividing the NDVI of the high-N plot (N-rich strip) by the NDVI of the zero-N plot or farmer's practice where less pre-plant N was applied (Arnall and al., 2016). RI values are used to predict yield and monitor top dress N fertilization. Many research has been carried out to determine the difference... S. Phillips, B. Arnall, M. Maatougui |
8. Creating a Comprehensive Software Framework for Sensor-driven Precision AgricultureRobots and GPS-guided tractors are the backbone of smart farming and precision agriculture. Many companies and vendors contribute to the market, each offering their own customized solutions for common tasks. These developments are often based on vendor-specific, proprietary components, protocols and software. Many small companies that produce sensors, actuators or software for niche applications could contribute their expertise to the global efforts of creating smart farming solutions, if their... O. Scholz, F. Uhrmann, M. Weule, T. Meyer, A. Gilson, J. Makarov, J. Hansen, T. Henties |
9. X-ray Imaging in Breeding and Harvesting ProcessesThe application of X-ray technology has a long tradition in different medical and technical fields. Compared to other sensor systems, its advantages lie in the capability to reveal structures within objects non-destructively. The analysis of X-ray images with image processing methods is applied for quality control, the detection of foreign objects or damages and other anomalies (e.g. in organs or bones). Until recently, the application of X-ray was mainly constrained to stationary applications... M. Weule, E. Hufnagel, J. Claussen, A. Berghaus, S. Burkhart, P. Noack, S. Gerth |
10. Effects of Crop Rotation on In-season Estimation of Optimal Nitrogen Rates for Corn Based on Proximal and Remote Sensing DataA remote sensing and calibration strip-based precision nitrogen (N) management (RS-CS-PNM) strategy has been developed by the Precision Agriculture Center at the University of Minnesota to provide in-season N recommendation rates based on satellite imagery. This strategy involves the application of multiple N rates before planting and the identification of the agronomic optimum N rate (AONR) at V7-V8 growth stages using normalized difference vegetation index (NDVI) calculated using satellite imagery.... A.C. Morales, D. . Quinn, K. Mizuta, Y. Miao |
11. High Throughput Phenotyping of the Energy Cane Crop UAV-based LiDAR, Multispectral and RGB DataEnergy cane is a hybrid of sugarcane cultivated for their high biomass and fiber instead of sugar. It is used for production of biofuels and as feedstock for animals. As a relatively new crop, accurate knowledge of biophysical parameters such as height and biomass of different genotypes are pertinent to cultivar development. Such knowledge is also crucial to manage crop health, understand response to environmental effects, optimize harvest schedules, and estimate bioenergy yield. Nonetheless,... B. Ghansah, I. Khuimphukhieo, J.L. Scott, M. Bhandari, J. Foster, J. Da silva, H. Li, M. Starek |
12. Evaluating the Impact of Irrigation Rate, Timing, and Maturity-based Cotton Cultivars on Yield and Fiber Quality in West TexasIn West Texas, effective irrigation is crucial for sustainable cotton production given the water scarcity from the declining Ogallala aquifer and erratic rainfall patterns. A three-year study (2020-2022) investigated irrigation rate and timing effects on early to mid-season cotton maturity groups. Five treatments, including rainfed (W1 or LLL) and variations in irrigation rates at growth stages (P1 to P4), were applied. Evaluation involved six to seven cotton cultivars from four maturity groups,... O. Adedeji, R. Karn, B.P. Ghimire, W. Guo, E.N. Wieber |