Proceedings

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Randhawa , R
Jiang, H
Nelson, K.J
Jacquin, A
Ryu, M
Ng, C
Rosin, N.A
Reich, R.M
Rasheed, R
Jonjak, A.K
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Authors
Bazzi, C.L
Souza, E.G
Khosla, R
Reich, R.M
Chung, S
Kim, K
Huh, Y
Hur, S
Ha, S
Ryu, M
Kim, H
Han, K
Randhawa , R
Jonjak, A.K
Adamchuk, V.I
Wortmann, C.S
Shapiro, C.A
Fergugson, R.B
Jacquin, A
Sigel, G
Hagolle, O
Lepoivre, B
Roumiguié, A
Poilvé, H
Danford, D.D
Nelson, K.J
Rhea, S.T
Stelford, M.W
Ferreyra, R
Wilson, J.A
Craker, B.E
Ashraf, E
Shurjeel, H.K
Rasheed, R
Li, D
Jiang, H
Chen, S
Wang, C
Amaral, L.R
Oldoni, H
Melo, D.D
Rosin, N.A
Alves, M.R
Demattê, J.M
Scudiero, E
Nugent, C.I
Ng, C
Jones, N
Azzam, T
Salunga, N.G
Lemus, S
Topics
Precision Nutrient Management
Precision Horticulture
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Sensor Application in Managing In-season CropVariability
Big Data, Data Mining and Deep Learning
Education and Outreach in Precision Agriculture
In-Season Nitrogen Management
Geospatial Data
Education of Precision Agriculture Topics and Practices
Type
Poster
Oral
Year
2012
2010
2014
2018
2024
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Authors

Filter results10 paper(s) found.

1. A Statistical and an Agronomic Approach for Definition of Management Zones in Corn and Soybean

The use of productivity level management zones (MZ) has demonstrated good potential for the site-specific management of crop inputs in traditional row crops. The objectives of this research were to analyze the process of defining MZs and develop methods to evaluate the quality of MZ maps. Two approaches were used to select the layers to be used in the MZ definition: 1) Statistical Approach (SA_MZ) and 2) Agronomic Approach (AA_MZ). The difference is that in the AA_MZ approach all non stable variables... C.L. Bazzi, E.G. Souza, R. Khosla, R.M. Reich

2. Determination of Sensor Locations for Monitoring of Greenhouse Ambient Environment

In protected crop production facilities such as greenhouse and plant factory, f... S. Chung, K. Kim, Y. Huh, S. Hur, S. Ha, M. Ryu, H. kim, K. han

3. Spectral Characterization to Discriminate Grass Weeds from Wheat Crop Using Remote Sensing and GIS for Precision Agriculture and Environmental Sustainability

Kaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj ... R. Randhawa

4. A Comparison Of Conventional And Sensor-based Lime Requirement Maps

Successful variable-rate applications of agricultural inputs, such as lime, rely on quality of input data. Systematic soil sampling is... A.K. Jonjak, V.I. Adamchuk, C.S. Wortmann, C.A. Shapiro, R.B. Fergugson

5. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time Series

An index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as... A. Jacquin, G. Sigel, O. Hagolle, B. Lepoivre, A. Roumiguié, H. Poilvé

6. ADAPT: A Rosetta Stone for Agricultural Data

Modern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote effi­cien­cies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker

7. Precision Agriculture: A Paradigm Shift for Espousal of Advanced Farming Practices Among Progressive Farmers in Punjab –Pakistan

Precision agriculture provides innovative farm information tools for improved decision making regarding crop growth and yield. Creating awareness for future applications of precision agriculture among progressive farmers in Pakistan was an instrumental force to conduct this study. The purpose was to appraise the awareness level of the respondents for applications of precision agriculture in the field. The objectives such as assessing the awareness level, available information sources, future needs,... E. Ashraf, H.K. Shurjeel, R. Rasheed

8. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data

This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang

9. Yield Potential Zones and Their Relationship with Soil Taxonomic Classes and Management Zones

The use of management zones (MZ) to subdivide agricultural areas based on the variability of yield potential and production factors is increasingly being explored by scientific research and demanded by farmers. However, there is still much uncertainty about which layers of information and procedures should be adopted for this purpose. Thus, our goal was to demonstrate whether simplistic approaches to creating MZ can satisfactorily address the variability of yield potential and soil classes. For... L.R. Amaral, H. Oldoni, D.D. Melo, N.A. Rosin, M.R. Alves, J.M. Demattê

10. Cultivating Future Leaders in Sustainable Agriculture: Insights from the Digital Agriculture Fellowship Program at the University of California, Riverside

Funded by USDA's National Institute of Food and Agriculture’s Sustainable Agricultural Systems Program and housed at the University of California, Riverside (UCR), the Digital Agriculture Fellowship (DAF) aims at equipping undergraduate students with the knowledge and experience necessary to meet the agricultural challenges posed by climate change and sustainability concerns. The program was established in 2020 and will be funded through 2026. Activities span over fifteen months for... E. Scudiero, C.I. Nugent, C. Ng, N. Jones, T. Azzam, N.G. Salunga, S. Lemus