Proceedings

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Hoffmann, W.C
Hopkins, B
Zillmann, E
Nagy, J
Kaiser, D
Grisham, M.P
Sekhon, B.S
Stępień, M
Saiz-Rubio, V
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Authors
Sekhon, B.S
Mukherjee, J
Sharma, A
Thind, S.K
Kaur, R
Makkar, M.S
Huang, Y
Hoffmann, W.C
Lan, Y
Thomson, S.J
Fritz, B.K
Lan, Y
Zhang, H
Yang, C
Martin, D
Lacey, R
Huang, Y
Hoffmann, W.C
Moulton, P
Lan, Y
Hoffmann, W.C
Westbrook, J
Zaller, M
Makkar, M.S
Kaul, A
Kumar, R
Sharma, A
Sekhon, B.S
Pannu, C.S
Walsh, O.S
Samborski, S.M
Stępień, M
Gozdowski, D
Lamb, D.W
Gacek, E.S
Drzazga, T
Walsh, O.S
Samborski, S.M
Gozdowski, D
Stępień, M
Leszczyńska, E
Frotscher, K.J
Schacht, R
Smith, L
Zillmann, E
Mulla, D
Zermas, D
Kaiser, D
Bazakos, M
Papanikolopoulos, N
Stanitsas, P
Morellas, V
Mulla, D
Laacouri, A
Kaiser, D
Johnson, R.M
Grisham, M.P
Saiz-Rubio, V
Diago, M
Tardaguila, J
Gutierrez, S
Rovira-Más, F
Alves, F
Bodnár, K.B
Nagy, J
Gombos, B
Rátonyi, T
Ragán, P
Sulyok, D
Nagy, J
Harsányi, E
Vántus, A
Csatári, N
Ragán, P
Harsányi, E
Nagy, J
Ágnes, T
Rátonyi, T
Vántus, A
Csatári, N
Nándor, C
Rátonyi, T
Harsányi, E
Ragán, P
Hagymássy, Z
Nagy, J
Vántus, A
Kerry, R
Shumate, S
Ingram, B
Hammond, K
Gunther, D
Jensen, R
Schill, S
Hansen, N
Hopkins, B
Topics
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
Remote Sensing Applications in Precision Agriculture
Modeling and Geo-statistics
Precision Nutrient Management
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Unmanned Aerial Systems
Spatial Variability in Crop, Soil and Natural Resources
Robotics, Guidance and Automation
Smart Weather for Precision Agriculture
On Farm Experimentation with Site-Specific Technologies
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Precision Dairy and Livestock Management
Drainage Optimization and Variable Rate Irrigation
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
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Filter results17 paper(s) found.

1. Development Of Unmanned Aerial Vehicles For Site-specific Crop Production Management

... Y. Huang, W.C. Hoffmann, Y. Lan, S.J. Thomson, B.K. Fritz

2. Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field Mapping

  A wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by synthesizing... Y. Lan, H. Zhang, C. Yang, D. Martin, R. Lacey, Y. Huang, W.C. Hoffmann, P. Moulton

3. Development Of A Decision Support System For Precision Areawide Pest Management In Cotton Production

  Crop models simulate growth and development, and provide relevant information for the routine management of the crop.  The use of crop models on large areas for diagnosing crop growing conditions or predicting crop production is hampered by the lack of sufficient spatial information about model inputs. Integrating crop models with other information technologies such as geographic information systems (GIS), variable rate technology, remote sensing, and global positioning... Y. Lan, W.C. Hoffmann, J. Westbrook, M. Zaller

4. Spectral Models for Estimation of Chlorophyll Content, Nitrogen, Moisture Stress and Growth of Wheat Crop

  Field  experiments  were  conducted  during  2009-10  and  2010-11 at  research  farm  of the department of Farm Machinery and Power Engineering, Punjab Agricultural university, Ludhiana.  Three wheat ... B.S. Sekhon, J. Mukherjee, A. Sharma, S.K. Thind, R. Kaur, M.S. Makkar

5. Estimation Of Nitrogen And Chlorophyll Content In Wheat Crop Using Hand Held Sensors

A Field experiment was conducted to estimate crop nitrogen (N) status and chlorophyll content in wheat crop by using chlorophyll content meter(Apogee’s CCM-200) and N-Tester®  (Make YARA International). The experiment was conducted by sowing university recommended wheat variety viz. PBW 550 with 5 nitrogen levels i.e. 0, 30, 60, 90, 120 & 150 kg N/ha. It was found that at tillering stage when nitrogen rates were increased from 0 to 150 kg ha-1 , the... M.S. Makkar, A. Kaul, R. Kumar, A. Sharma, B.S. Sekhon, C.S. Pannu

6. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing Recommendations

Active optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga

7. On-Farm Evaluation of an Active Optical Sensor Performance for Variable Nitrogen Application in Winter Wheat

Winter wheat (Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management strategies... O.S. Walsh, S.M. Samborski, D. Gozdowski, M. Stępień, E. Leszczyńska

8. Planet Labs' Monitoring Solution in Support of Precision Agriculture Practices

Satellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann

9. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer Vision

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas

10. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in Corn

Low altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser

11. Assessing the Variability of Red Stripe Disease in Louisiana Sugarcane Using Precision Agriculture Methods

Symptoms of red stripe disease caused by Acidovorax avenae subsp. avenae in Louisiana between 1985 and 2010 were limited to the leaf stripe form which caused no apparent yield loss.  During 2010, the more severe top rot form was observed, and a study was initiated to investigate the distribution of red stripe in the field and determine its effects on cane and sugar yields. Two fields of cultivar HoCP 00-950, one plant-cane (PC) crop and one first-ratoon (FR) crop, affected by top rot were... R.M. Johnson, M.P. Grisham

12. Canopy Temperature Mapping with a Vineyard Robot

The wine industry is a strategic sector in many countries worldwide. High revenues in the wine market typically result in higher investments in specialized equipment, so that producers can introduce disruptive technology for increasing grape production and quality. However, many European producers are approaching retirement age, and therefore the agricultural sector needs a way for attracting young farmers who can assure the smooth transition between generations; digital technology offers an opportunity... V. Saiz-rubio, M. Diago, J. Tardaguila, S. Gutierrez, F. Rovira-más, F. Alves

13. Correlations Between Meteorological Parameters and the Water Loss of Maize from Silking to Harvesting

The University of Debrecen provides outstanding conditions for the development of “Smart Weather for Precision Agriculture” programs. The reliability of research is provided by the Polyfactoral Long-term Field Experiments of Debrecen (hybrid x fertilisation x plant density x tillage x irrigation) established in 1983. Within this research program, it is possible to examine various crop cultures, cultivars and hybrids under changing natural, environmental and weather circumstances,... K.B. Bodnár, J. Nagy, B. Gombos

14. Evaluation of Strip Tillage Systems in Maize Production in Hungary

Strip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the soil.... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári

15. Examining the Relationship Between SPAD, LAI and NDVI Values in a Maize Long-Term Experiment

In Hungary, the preconditions for the use of precision crop production have undergone enormous development over the last five years. RTK coverage is complete in crop production areas. Consultants are increasingly using the vegetation index maps from Landsat and Sentinel satellite data, but measurements with on-site proximal plant sensors are also needed to exclude the influence of the atmosphere. The aim of our studies was to compare the values measured by proximal plant sensors in the... P. Ragán, E. Harsányi, J. Nagy, T. Ágnes, T. Rátonyi, A. Vántus, N. Csatári

16. The Spread of Precision Livestock Farming Technology at Dairy Farms in East Hungary

During the survey, 25 dairy farms were examined in East Hungary in Hajdú-Bihar (H-B) County between 2017 and 2018 by methodical observation and oral interviews with the farm managers, about the spread of Precision Livestock Farming (PLF) technologies. Among Holstein Friesian dairy farms in the County 60% were questioned, and the representativity was above 47 percent ins each size category. Nine precision farming equipment were examined on the farms: milking robot or robotic carousel milking... C. Nándor, T. Rátonyi, E. Harsányi, P. Ragán, Z. Hagymássy, J. Nagy, A. Vántus

17. Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation Management

The Western United States is currently experiencing a “Mega Drought”. This makes efficient water use more important than ever. Turfgrass is a major vegetation type in urban areas and performs many ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere and reducing wild-fire risk. There are now more acres of irrigated turfgrass (>40 million) in the USA than irrigated corn, wheat and fruit trees combined (Milesi et al., 2005). It has been... R. Kerry, S. Shumate, B. Ingram, K. Hammond, D. Gunther, R. Jensen, S. Schill, N. Hansen, B. Hopkins