Proceedings
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| Filter results7 paper(s) found. |
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1. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast ChinaCrop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in Northeast... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth |
2. Factors Influencing the Timing of Precision Agriculture Technology Adoption in Southern U.S. Cotton ProductionTechnology innovators in cotton production adopted precision agriculture (PA) technologies soon after they became commercially available, while others adopted these technologies in later years after evaluating the success of the innovators. The timing of... D.M. Lambert, J.A. Larson, B.C. English, R.M. Rejesus, M.C. Marra, A.K. Mishra, C. Wang, P. Watcharaanantapong, R.K. Roberts, M. Velandia |
3. In-season Diagnosis of Rice Nitrogen Status Using an Active Canopy Sensor... Y. Yao, Y. Miao, S. Huang, M. Gnyp, R. Khosla, R. Jiang, G. Bareth |
4. Using Geospatial Data to Assess How Climate Change May Affect Land Suitability for Agriculture ProductionFinding solutions to the challenge of sustainably feeding the world’s growing population is a pressing research need that cuts across many disciplines including using geospatial data. One possible area could be developing agricultural frontiers. Frontiers are defined as land that is currently not cultivated but that may become suitable for agriculture under climate change. Climate change may drive large-scale geographic shifts in agriculture, including expansion in cultivation at the thermal... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan |
5. Proximal Sensing of Penetration Resistance at a Permanent Grassland Site in Southern FinlandProximal soil sensing allows for assessing soil spatial heterogeneity at a high spatial resolution. These data can be used for decision support on soil and crop agronomic management. Recent sensor systems are capable of simultaneously mapping several variables, such as soil electrical conductivity (EC), spectral reflectance, temperature, and water content, in real-time. In autumn 2021, we used a commercial soil scanner (Veris iScan+) to derive information on soil spatial variability for a permanent... H.E. Ahrends, A. Lajunen |
6. Spectral Imaging Deep Learning Mapper for Precision AgricultureWith the growing variety of RGB cameras, spectral sensors, and platforms like field robots or unmanned aerial vehicles (UAV) in precision agriculture, there is a demand for straightforward utilization of collected field data. In recent years, deep learning has gained significant attention and delivered impressive results in the realm of computer vision tasks, such as semantic segmentation. These models have also found extensive applications in research related to precision agriculture and spectral... L. Thomas, B. Jakimow, A. Janz, P. Hostert, A. Lajunen |
7. Affordable Telematics System for Recording and Monitoring Operational Data in Crop FarmingThe aim of this research was to create an affordable telematics system for agricultural tractors for enhancing existing data logging capabilities. This system enables real-time transmission of operational data from the tractor's CAN bus to a server for storage, monitoring, and further analysis. By leveraging standardized communication protocols like ISO 11783 and J1939, operational data such as fuel consumption and engine load can be easily monitored. The system was built around a Raspberry... A. Lajunen, H. Hovio |