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T, S
Hernandez, C
Verschwele, A
Zhu, Y
Solie, J.B
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Authors
T, S
giriyappa, M
Hanumanthappa, D
Dr., N
K, S
Yogananda, S
Kiran, A
Roberts, D.C
Brorsen, B.W
Raun, W.R
Solie, J.B
Taylor, R.K
Bennur, P
Solie, J.B
Wang, N
Weckler, P
Raun, W.R
Liu, X
Cao, Q
Tian, Y
Zhu, Y
Zhang, Z
Cao, W
Li, S
Cao, Q
Liu, X
Tian, Y
Zhu, Y
Prestholt, A
Hernandez, C
Ciampitti , I
Kyveryga, P
Rydahl, P
Boejer, O
Torresen, K
Montull, J.M
Taberner, A
Bückmann, H
Verschwele, 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
van Versendaal, E
Hernandez, C
Kyveryga, P
Ciampitti, I
Hernandez, C
Kyveryga, P
Correndo, A
Prestholt, A
Ciampitti, I
Magalhaes Cisdeli, P
Nocera Santiago, G.N
Ciampitti, I
Hernandez, C
Lucero, M.F
Zajdband, A
Hernandez, C
Ciampitti, I
CARCEDO, A
Hernandez, C
Correndo, A
Lacasa, J
Magalhaes Cisdeli, P
Nocera Santiago, G.N
Ciampitti, I
Cano, P.B
CARCEDO, A
Gomez, F
Hernandez, C
Gimenez, V
Ciampitti, I
Topics
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing for Nitrogen Management
Applications of Unmanned Aerial Systems
In-Season Nitrogen Management
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Decision Support Systems
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Weather and Models for Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Geospatial Data
Decision Support Systems
Country Representative Report
Type
Oral
Poster
Year
2016
2008
2018
2022
2024
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Authors

Filter results16 paper(s) found.

1. Spatial Variability of Soil Nutrients and Site Specific Nutrient Management in Maize

A field study was conducted during kharif 2014 and rabi 2014-15 at Southern Transition Zone of Karnataka under the jurisdiction of University of Agricultural Sciences, GKVK, Bangalore, India to know the spatial variability for available nutrient content in cultivator’s field and effect of site specific nutrient management in maize. The farmer’s fields have been delineated with each grid size of 50 m x 50 m using geospatial technology. Soil samples from 0-15 cm were... S. T, M. Giriyappa, D. Hanumanthappa, N. Dr., S. K, S. Yogananda, A. Kiran

2. Prediction of Nitrogen Needs with Nitrogen-rich Strips and Ramped Nitrogen Strips

Both nitrogen rich strips and ramped nitrogen strips have been used to estimate topdress nitrogen needs for winter wheat based on in-season optical reflectance data. The ramped strip system places a series of small plots in each field with increasing levels of nitrogen to determine the application rate at which predicted yield response to nitrogen reaches a plateau. The nitrogen-rich strip system uses a nitrogen fertilizer optimization algorithm based on optical reflectance measures from the nitrogen-rich... D.C. Roberts, B.W. Brorsen, W.R. Raun, J.B. Solie

3. Controller Performance Criteria for Sensor Based Variable Rate Application

Sensor based variable rate application of crop inputs provides unique challenges for traditional rate controllers when compared to map based applications. The controller set point is typically changing every second whereas with a map based systems the set point changes much less frequently. As applied data files for a sensor based variable rate nitrogen applicator were obtained from a wheat field in north central Oklahoma. These data were analyzed to determine the magnitude and frequency of rate... R.K. Taylor, P. Bennur, J.B. Solie, N. Wang, P. Weckler, W.R. Raun

4. 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

5. 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

6. Analytical and Technological Advancements for Soybean Quality Mapping and Economic Differentiation

In the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategies. Recent... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga

7. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 Countries

Reducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: occurrence... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele

8. 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

9. Potential Benefits of Variable Rate Nitrogen Topdressing Strategy Coupled with Zoning Technique: a Case Study in a Town-scale Rice Production System

Integrating remote sensing (RS)-based variable rate nitrogen (N) recommendation (VRNR) algorithms and management zones (MZs) may improve the accuracy and efficiency of site-specific N management. However, its potential benefits for application in commercial rice production systems can hardly be assessed, since it requires to intervene in common agricultural practices and causes certain economic and environmental consequences. Through a machine learning approach, this study aims to comprehensively... J. Zhang, W. Wang, Z. Fu, Q. Cao, Y. Tian, Y. Zhu, W. Cao, X. Liu

10. Optimizing Nitrogen Application in Global Wheat Production by an Integrated Bayesian and Machine Learning Approach

Wheat production plays a pivotal role in global food security, with nitrogen fertilizer application serving as a critical factor. The precise application of nitrogen fertilizer is imperative to maximize wheat yield while avoiding environmental degradation and economic losses resulting from excess or inadequate usage. The integration of Bayesian and machine learning methodologies has gained prominence in the realm of agricultural research. Bayesian and machine learning based methods have great... Z. Liu, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao

11. Spatio-temporal Variability of Intra-field Productivity Using Remote Sensing

Understanding the spatiotemporal variability in intra-farm productivity is crucial for management in making agronomic decisions. Furthermore, these decision-making processes can be enhanced using spatial data science and remote sensing. This study aims to develop a framework to asses the spatio-temporal variability of intra-farm productivity through historical satellite data and climate data. Historical satellite data and rainfall information from diverse fields across the United States (2016-2022)... E. Van versendaal, C. Hernandez, P. Kyveryga, I. Ciampitti

12. Spatial Predictive Modeling to Quantify Soybean Seed Quality Using Remote Sensing and Machine Learning

In recent years, the advancement of artificial intelligence technologies combined with satellite technology is revolutionized agriculture through the development of algorithms that help producers become more sustainable. This could improve the conditions of farmers not only by maximizing their production and minimizing environmental impact but also due to better economic benefits by allowing them to access high-value-added markets. Furthermore, the use of predictive tools that could improve the... C. Hernandez, P. Kyveryga, A. Correndo, A. Prestholt, I. Ciampitti

13. A Digital Interactive Decision Dashboard to Analyze, Store and Share Year-to-year Crop Genotype Yield

The lag time between data collection and sharing is a critical bottleneck in order to make impactful decision at farmer field-scale. Following this line, there is a need for developing a digital interactive decision dashboard for sharing results of crop trials, in parallel to establish a database for storing data. These crop trials, invaluable for farmers seeking to determine the optimal genotype for their crops, are at risk of becoming obsolete due to the current format and the lack of more near... P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti, C. Hernandez

14. Using Remote Sensing to Quantify Biomass in Alfalfa

Satellite images are a useful decision support tool to optimize management practices at on-farm scale. Based on this, the development of predictive tools to estimate pasture biomass can be a promising framework to determine the best cutting time, maximizing biomass without compromising yield parameters. Therefore, the main objective of this study was to develop a regression model that allows estimating a value of biomass to give as a recommendation to farmers. To collaborate in their decision... M.F. Lucero, A. Zajdband, C. Hernandez, I. Ciampitti, A. Carcedo

15. From Scientific Literature to the End User: Democratizing Access to Data Products Through Interactive Applications

In recent years, the sustained advance in the creation of powerful programming libraries is allowing not only the creation of complex models with predictive capabilities but also revolutionizing visualization processes and the deployment of interactive applications. Some of these tools, such as Streamlit or Shiny frameworks in languages such as Python or R, allow us to create from simple applications with friendly interfaces to complex tools. These interactive digital decision dashboards allow... C. Hernandez, A. Correndo, J. Lacasa, P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti

16. Trends in Agricultural Technology Advancements: Insights from US Patent Analysis

Meeting the demand for food, fiber, and fuel production while addressing environmental concerns and enhancing societal benefits underscores the need to transition to conservation approaches and sustainable intensification pathways in current agricultural cropping systems. Technological advances in agriculture offer promising opportunities to facilitate this transition. Following this rationale, this study aims to analyze prevailing trends in agricultural technology advancements. Active patents... P.B. Cano, A. Carcedo, F. Gomez, C. Hernandez, V. Gimenez, I. Ciampitti