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Lopes, W.C
Laursen, M.S
Esau, K
Liang, X
Lie, D.M
Landivar, J
E. Flores, A
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Authors
Lie, D.M
shilai, Y.M
Lopes, W.C
Domingues, G
Sousa, R.V
Porto, A.J
Inamasu, R.Y
Pereira, R.R
Vellidis, G
Liakos, V
Porter, W
Liang, X
Tucker, M.A
Esau, K
Zaman, Q
Farooque, A
Schumann, A
Liakos, V
Porter, W
Liang, X
Tucker, M
McLendon, A
Perry, C
Vellidis, G
Dyrmann, M
Skovsen, S
Jørgensen, R.N
Laursen, M.S
Christiansen, M.P
Laursen, M.S
Jørgensen, R.N
Skovsen, S
Gislum, R
Lacerda, L
Miao, Y
Sharma, V
E. Flores, A
Kechchour, A
Lu, J
Bhandari, M
Landivar, J
Ghansah, B
Zhao, L
Landivar, J
Pal, P
Topics
Precision Horticulture
Guidance, Robotics, Automation, and GPS Systems
Engineering Technologies and Advances
Decision Support Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2016
2018
2024
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Filter results9 paper(s) found.

1. Research on Nutrition and Quality Detection Technology of Soil, Leaf and Fruit of Citrus Based on and Digital Image Spectroscopic Techniques

The diagnosis technique of real-time lossless crop nutrition is the foundation and conditions for the precise, effective fertilization, cultivation and management, and so on. Currently, the diagnosis of crop nutrition mainly relies on the routine chemical analysis of laboratory. Due to the complicated procedure, time-consuming,... D.M. Lie, Y.M. Shilai

2. Compatible ISOBUS Applications Using a Computational Tool for Support the Phases of the Precision Agriculture Cycle

... W.C. Lopes, G. Domingues, R.V. Sousa, A.J. Porto, R.Y. Inamasu, R.R. Pereira

3. A Dynamic Variable Rate Irrigation Control System

Currently variable rate irrigation (VRI) prescription maps used to apply water differentially to irrigation management zones (IMZs) are static.  They are developed once and used thereafter and thus do not respond to environmental variables which affect soil moisture conditions.  Our approach for creating dynamic prescription maps is to use soil moisture sensors to estimate the amount of irrigation water needed to return each IMZ to an ideal soil moisture condition.  The UGA Smart... G. Vellidis, V. Liakos, W. Porter, X. Liang, M.A. Tucker

4. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry Harvesting

Wild blueberries are an important horticultural crop native to northeastern North America. Management of wild blueberry fields has improved over the past decade causing increased plant density and leaf foliage. The majority of wild blueberry fields are picked mechanically using tractor mounted harvesters with 16 rotating rakes that gently comb through the plants. The extra foliage has made it more difficult for the cleaning brush to remove unwanted debris (leaf, stems, weeds, etc.) from the picker... K. Esau, Q. Zaman, A. Farooque, A. Schumann

5. Three Years of On-Farm Evaluation of Dynamic Variable Rate Irrigation: What Have We Learned?

This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015, 2016 and 2017 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed within... V. Liakos, W. Porter, X. Liang, M. Tucker, A. Mclendon, C. Perry, G. Vellidis

6. Using a Fully Convolutional Neural Network for Detecting Locations of Weeds in Images from Cereal Fields

Information about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying. For detecting weeds, this study proposes a fully convolutional neural network, which detects weeds in images and classifies each one as either a monocot or dicot. The network has been trained on over 13 000 weed annotations... M. Dyrmann, S. Skovsen, R.N. Jørgensen, M.S. Laursen

7. Ground Vehicle Mapping of Fields Using LiDAR to Enable Prediction of Crop Biomass

Mapping field environments into point clouds using a 3D LIDAR has the ability to become a new approach for online estimation of crop biomass in the field. The estimation of crop biomass in agriculture is expected to be closely correlated to canopy heights. The work presented in this paper contributes to the mapping and textual analysis of agricultural fields. Crop and environmental state information can be used to tailor treatments to the specific site. This paper presents the current results... M.P. Christiansen, M.S. Laursen, R.N. Jørgensen, S. Skovsen, R. Gislum

8. Estimating Water and Nitrogen Deficiency in Corn Using a Multi-parameter Proximal Sensor

The Crop Circle Phenom (CCP) is an innovative integrated proximal sensor that can be potentially used to perform in-season diagnosis of nitrogen and water status. In addition to measuring spectral reflectance in several bands including the red, red edge, and near-infrared wavelengths, the CCP can also measure canopy and air temperatures and provides several parameters that can be associated with chlorophyll content, crop vigor, and water status. These capabilities differentiate the CCP from other... L. Lacerda, Y. Miao, V. Sharma, A. E. flores, A. Kechchour, J. Lu

9. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySat

Satellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari