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Downing, B
Tian, Y
Zebrath, B
Jagadish, K
Dill, T
Trevisan, R.G
Jha, S
Zaller, M
Zhou, S
Krmenec, A
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Authors
Chantuma, D
Zaller, M
Lan, Y
Hoffmann, W.C
Westbrook, J
Zaller, M
Eitelwein, M.T
Molin, J.P
Spekken, M
Trevisan, R.G
Lianqing, Z
Zhou, S
Songchao, C
Yafei, Y
Liu, X
Cao, Q
Tian, Y
Zhu, Y
Zhang, Z
Cao, W
Cambouris, A
Lajili, A
Chokmani , K
Perron, I
Adamchuk, V
Biswas , A
Zebrath, B
Li, S
Cao, Q
Liu, X
Tian, Y
Zhu, Y
Jha, S
Saraswat, D
Ward, M.D
Stelford, M
Krmenec, A
Miao, Y
liu, X
Tian, Y
Zhu, Y
Cao, W
Cao, Q
Chen, X
Li, Y
Zhang, J
Wang, W
Fu, Z
Cao, Q
Tian, Y
Zhu, Y
Cao, W
liu, X
Liu, Z
liu, X
Tian, Y
Zhu, Y
Cao, W
Cao, Q
Waltz, L
Katari, S
Khanal, S
Dill, T
Porter, C
Ortez, O
Lindsey, L
Nandi, A
Castiblanco Rubio, F.A
Arun, A
Lee, B
Balmos, A
Jha, S
Krogmeier, J
Love, D.J
Buckmaster, D
Bari, M.A
Bakshi, A
Witt, T
Caragea, D
Jagadish, K
Felderhoff, T
Pramanik, S
Choton, J
Waltz, L
Khanal, S
Katari, S
Hong, C
Anup, A
Colbert, J
Potlapally, A
Dill, T
Porter, C
Engle, J
Stewart, C
Subramoni, H
Machiraju, R
Ortez, O
Lindsey, L
Nandi, A
Jha, S
Krogmeier, J
Buckmaster, D
Love, D.J
Grant, R.H
Crawford, M
Brinton, C
Wang, C
Cappelleri, D
Balmos, A
Downing, B
Topics
Spatial Variability in Crop, Soil and Natural Resources
Modeling and Geo-statistics
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Applications of Unmanned Aerial Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
On Farm Experimentation with Site-Specific Technologies
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Wireless Sensor Networks and Farm Connectivity
Robotics and Automation with Row and Horticultural Crops
Type
Poster
Oral
Year
2010
2014
2016
2018
2022
2024
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Authors

Filter results18 paper(s) found.

1. Carbohydrate Reserves On Tapping Systems And Production Of Hevea Brasiliensis

CARBOHYDRATE RESERVES ON TAPPING SYSTEMS AND PRODUCTION OF Hevea brasiliensis Chantuma P1., Lacointe A2., Kasempsap P3., Thanysawanyangkura S4., Gohet E5., Clément A6., Guilliot A7., Améglio T2., Thaler P8. and Chantuma A1. 1 Agriculture Scientist Senior, Chachoengsao Rubber Research Center, RRIT-DOA, Ministry of Agriculture and Cooperative, Sanam Chai Ket, Thailand. 2 INRA, UMR 547 PIAF, F-60100 Clermont-Ferrand, France. 3 Department... D. Chantuma, M. Zaller

2. 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

3. Assessing Definition Of Management Zones Trough Yield Maps

Yield mapping is one of the core tools of precision agriculture, showing the result of combined growing factors. In a series of yield maps collected along seasons it is possible to observe not only the spatial distribution of the productivity but also its spatial consistency among different seasons. This work proposes the study of distinct methods to analyze yield stability in grain crops regarding its potential for defining management zones from a historical sequence of yield maps. Two methods... M.T. Eitelwein, J.P. Molin, M. Spekken, R.G. Trevisan

4. Development of Micro-tractor-based Measurement Device of Soil Organic Matter Using On-the-go Visual-near Infrared Spectroscopy in Paddy Fields of South China

Soil organic matter (SOM) is an essential soil property for assessing the fertility of paddy soils in South China. In this study, a set of micro-tractor-based on-the-go device was developed and integrated to measure in-situ soil visible and near infrared (VIS–NIR) spectroscopy and estimate SOM content. This micro-tractor-based on-the-go device is composed of a micro-tractor with toothed-caterpillar band, a USB2000+ VIS–NIR spectroscopy detector, a self-customized steel plow and a self-customized... Z. Lianqing, S. Zhou, C. Songchao, Y. Yafei

5. Using Unmanned Aerial Vehicle and Active-Optical Sensor to Monitor Growth Indices and Nitrogen Nutrition of Winter Wheat

Using unmanned aerial vehicle (UAV) remote sensing monitoring system can rapidly and cost-effectively provide crop canopy information for growth diagnosis and precision fertilizer regulation. RapidScan CS-45 (Holland, Lincoln, NE, USA) is a portable active-optical sensor designed for timely, non-destructive obtaining plant canopy information without being affected by weather condition. UAV equipped with RapidScan, is of great significant for rapidly monitoring crop growth and nitrogen (N) status.... X. Liu, Q. Cao, Y. Tian, Y. Zhu, Z. Zhang, W. Cao

6. Use of Proximal Soil Sensing to Delineate Management Zones in a Commercial Potato Field in Prince Edward Island, Canada

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties. Such MZs can often be used for site-specific management of crop production inputs. The purpose of this study was to determine the efficiency of two proximal soil sensors for delineating MZs in an 8.1-ha commercial potato (Solanum tuberosum L.) field in Prince Edward Island (PEI), Canada. A galvanic contact resistivity sensor (Veris-3100 [Veris]) and electromagnetic induction sensors... A. Cambouris, A. Lajili, K. Chokmani , I. Perron, V. Adamchuk, A. Biswas , B. Zebrath

7. Using a UAV-Based Active Canopy Sensor to Estimate Rice Nitrogen Status

Active canopy sensors have been widely used in the studies of crop nitrogen (N) estimation as its suitability for different environmental conditions. Unmanned aerial vehicle (UAV) is a low-cost remote sensing platform for its great flexibility compared to traditional ways of remote sensing. UAV-based active canopy sensor is expected to take the advantages of both sides. The objective of this study is to determine whether UAV-based active canopy sensor has potential for monitoring rice N status,... S. Li, Q. Cao, X. Liu, Y. Tian, Y. Zhu

8. Analyzing Trends for Agricultural Decision Support System Using Twitter Data

The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method to... S. Jha, D. Saraswat, M.D. Ward

9. Use of Precision Technologies to Conduct Successful Within-field, On-farm Trials

Performing randomized replicated trials in row crop field environments has the potential to increase crop production in environmentally sustainable ways.  Successful implementation requires an understanding of implement capabilities and sources of potential systematic error, including operator error.  Equipment capabilities can be thought of as a series of several critical “links in a chain,” each with implications that propagate downstream.   We will... M. Stelford, A. Krmenec

10. Developing a Wheat Precision Nitrogen Management Strategy by Combining Satellite Remote Sensing Data and WheatGrow Model

Precision nitrogen (N) management (PNM) is becoming increasingly popular due to its ability to synchronize crop N demand with soil N supply spatiotemporally. The previous evidence has demonstrated that variable rate fertilization contributes to achieving high yields and high efficiencies. However, PNM at the regional level remains unclear and challenging. This study aims to develop a novel management zone (MZ)-based PNM strategy (MZ-PNM) to optimize the basal and topdressing N rates at the regional... Y. Miao, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao, X. Chen, Y. Li

Showing 1 to 10 of 18 entries