Proceedings

Find matching any: Reset
Amin, S
Phillips, L
Pieger, K
Arias, A.C
Videla, H
Yang, X
Choi, D
Add filter to result:
Authors
Phillips, L
Lee, W
Ehsani, R
Roka, F
Choi, D
Yang, C
Choi, D
Lee, W
Schueller, J.K
Ehsani, R
Roka, F.M
Ritenour, M.A
Scholz, O
Uhrmann, F
Gerth, S
Pieger, K
Claußen, J
Xu, X
Li, Z
Yang, G
Gu, X
Song, X
Yang, X
Feng, H
Goodrich, P.J
Baumbauer, C
Arias, A.C
Balboa, G
Degioanni, A
Bongiovanni, R
Melchiori, R
Cerliani, C
Scaramuzza, F
Bongiovanni, M
Gonzalez, J
Balzarini, M
Videla, H
Amin, S
Esposito, G
Topics
eXtension: Precision Agriculture on the Internet
Engineering Technologies and Advances
Sensor Application in Managing In-season Crop Variability
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Wireless Sensor Networks
Education and Outreach in Precision Agriculture
Type
Oral
Year
2010
2014
2016
2018
2022
Home » Authors » Results

Authors

Filter results7 paper(s) found.

1. Not Possible In Real Life: Precision Agriculture’s Future In 3D Virtual Worlds

Immersive 3D virtual worlds may be several years away from mainstream adoption, but thousands of scientists, educators, and visionary thinkers are already using these environments to network with colleagues, conduct research, create engaging simulations, and develop instructional models that can reach global audiences. Virtual reality offers the potential to create dynamic content that is either not possible to build in real life, or prohibitively expensive. Travel costs can be reduced by bringing... L. Phillips

2. Post-Harvest Quality Evaluation System On Conveyor Belt For Mechanically Harvested Citrus

Recently, a machine vision technology has shown its popularity for automating visual inspection. Many studies proved that the machine vision system can successfully estimate external qualities of fruit as good as manual inspection. However, introducing mechanical harvesters to citrus industry caused the following year’s yield loss due to the loss of immature young citrus. In this study, a machine vision system on a conveyor belt was developed to inspect mechanically... W. Lee, R. Ehsani, F. Roka, D. Choi, C. Yang

3. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning Technologies

World climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple cameras... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour

4. A Comparison of Three-Dimensional Data Acquisition Methods for Phenotyping Applications

Currently Phenotyping is primarily performed using two-dimensional imaging techniques. While this yields interesting data about a plant, a lot of information is lost using regular cameras. Since a plant is three-dimensional, the use of dedicated 3D-imaging sensors provides a much more complete insight into the phenotype of the plant. Different methods for 3D-data acquisition are available, each with their inherent advantages and disadvantages. These have to be addressed depending on the particular... O. Scholz, F. Uhrmann, S. Gerth, K. Pieger, J. Claußen

5. Using Canopy Hyperspectral Measurements to Evaluate Nitrogen Status in Different Leaf Layers of Winter Wheat

Nitrogen (N) is one of the most important nutrient matters for crop growth and has the marked influence on the ultimate formation of yield and quality in crop production. As the most mobile nutrient constituent, N always transfers from the bottom to top leaves under N stress condition. Vertical gradient changes of leaf N concentration are a general feature in canopies of crops. Hence, it is significant to effectively acquire vertical N information for optimizing N fertilization managements.... X. Xu, Z. Li, G. Yang, X. Gu, X. Song, X. Yang, H. Feng

6. A Passive-RFID Wireless Sensor Node for Precision Agriculture

Accurate soil data is crucial for precision agriculture.  While existing optical methods can correlate soil health to the gasses emitted from the field, in-soil electronic sensors enable real-time measurements of soil conditions at the effective root zone of a crop. Unfortunately, modern soil sensor systems are limited in what signals they can measure and are generally too expensive to reasonably distribute the sensors in the density required for spatially accurate feedback.  In this... P.J. Goodrich, C. Baumbauer, A.C. Arias

7. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in Argentina

The lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito