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Prassack, L
Lacerda, L.N
Usui, K
Connor, J
Sanderson, J
Poursina, D
Liu, Y
Stepien, P
Sadler, E
Lundström, C
Papanikolopoulos, N
Mora, H
Lowrance, C
Pan, L
Mohamed, M.M
Moon, J
Christensen, A
Tian, Y
Mangus, D.L
Majdi, M
Mennuti, D
Qian, B
Chudy, T
Lowenberg-DeBoer, J
Magalhães, D.V
Lambur, M
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Authors
Majdi, M
Benjamin, D
Marie-France, D
Adamchuk, V.I
Pan, L
Ferguson, R.B
Pan, L
Adamchuk, V.I
Martin, D.L
Schroeder, M.A
Fergugson, R.B
Moss, J.Q
Pan, X
Tian, Y
Hutchinson, A
Garcia-Torres, L
Gomez-Candon, D
Caballero-Novella, J.J
Pe, J.M
Jurado-Exp, M
Castillejo-Gonz, I
Garc, A
Lopez-Granados, F
Prassack, L
Lambur, M
Cao, Q
Miao, Y
Feng, G
Li, F
Liu, B
Gao, X
Liu, Y
Yang , W
Kim, S
Moon, J
Kim, D
Umeda, H
Shibusawa, S
Li, Q
Usui, K
Kodaira, M
Shibusawa, S
Umeda, H
Usui, K
Kodaira, M
Li, Q
Grocholski, P
Stepien, P
Kulczycki, G
Michalski, A
Mangus, D.L
Sharda, A
Lundström, C
Lindblom, J
Drew, P
Sudduth, K.A
Sadler, E
Vellidis, G
Lowrance, C
Fountas, S
Liakos, V
Mulla, D
Zermas, D
Kaiser, D
Bazakos, M
Papanikolopoulos, N
Stanitsas, P
Morellas, V
Becker, M
Velasquez, A.E
Guerrero, H.B
HIguti, V.A
Milori, D.M
Magalhães, D.V
Kyveryga, P.M
Pritsolas, J
Connor, J
Pearson, R
Gebbers, R
Dworak, V
Mahns, B
Weltzien, C
Büchele, D
Gornushkin, I
Mailwald, M
Ostermann, M
Rühlmann, M
Schmid, T
Maiwald, M
Sumpf, B
Rühlmann, J
Bourouah, M
Scheithauer, H
Heil, K
Heggemann, T
Leenen, M
Pätzold, S
Welp, G
Chudy, T
Mizgirev, A
Wagner, P
Beitz, T
Kumke, M
Riebe, D
Kersebaum, C
Wallor, E
Kyveryga, P.M
Fey, S
Connor, J
Kiel, A
Muth, D
Cheng, Z
Meng, J
Shang, J
Liu, J
Qian, B
Jing, Q
Wiseman, L
Sanderson, J
Celades, J.A
Caicedo, J.H
García, C.E
Mora, H
Mohamed, M.M
Zaman, Q
Esau, T
Farooque, A
Erickson, B.J
Lowenberg-DeBoer, J
Rai, N
Zhang, Y
Quanbeck, J
Christensen, A
Sun, X
Poursina, D
Brorsen, W
Mizuta, K
Miao, Y
Morales, A.C
Lacerda, L.N
Cammarano, D
Nielsen, R.L
Gunzenhauser, R
Kuehner, K
Wakahara, S
Coulter, J.A
Mulla, D.J
Quinn, D.
McArtor, B
Lacerda, L.N
Miao, Y
Mizuta, K
Stueve, K
Capolicchio, J
Mennuti, D
Milani, I
Fortunato, M
Petix, R
Reyes Gonzalez, J
Sunkevic, M
Ferreyra, R
Lehmann, J
Lowenberg-DeBoer, J
Topics
Food Security and Precision Agriculture
Precision A to Z for Practitioners
Modeling and Geo-statistics
Precision Horticulture
Remote Sensing Applications in Precision Agriculture
eXtension: Precision Agriculture on the Internet
Precision Nutrient Management
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change, Standards)
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Decision Support Systems in Precision Agriculture
Sensor Application in Managing In-season Crop Variability
Unmanned Aerial Systems
Engineering Technologies and Advances
Precision Nutrient Management
Profitability, Sustainability and Adoption
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Precision Agriculture and Global Food Security
Profitability and Success Stories in Precision Agriculture
Factors Driving Adoption
Big Data, Data Mining and Deep Learning
On Farm Experimentation with Site-Specific Technologies
In-Season Nitrogen Management
Precision Agriculture and Global Food Security
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
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Authors

Filter results31 paper(s) found.

1. Analysis Of Water Use Efficiency Using On-the-go Soil Sensing And A Wireless Network

An efficient irrigation system should meet the demands of the growing crops. While limited water supply may result in yield reduction, excess irrigation is a waste of resources. To investigate water use efficiency, on-the-go sensing technology was used to reveal soil spatial variability relevant to water holding capacity (in this example, field elevation and apparent electrical conductivity). These high-density data layers were used to identify strategic sites where monitoring water availability... L. Pan, V.I. Adamchuk, D.L. Martin, M.A. Schroeder, R.B. Fergugson

2. Normalized Difference Vegetative Index For Evaluating Turfgrass Color: A Comparison Of Two Handheld Devices

The normalized difference vegetative index (NDVI) is a commonly used light reflectance index in agriculture. For turfgrass research, color and herbicide phytotoxicity have historically been subjectively rated by human evaluators. Prior research has related NDVI to creeping bentgrass (Agrostis stolonifera L.) (R2 = 0.50) and tall fescue (Festuca arundinacea Schreb) (R2 = 0.80) color, and bermudagrass [Cynodon dactylon... J.Q. Moss, X. Pan, Y. Tian, A. Hutchinson

3. Management Of Remote Imagery For Precision Agriculture

Satellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack

4. The Scholarship Of eXtension

  eXtension (www.extension.org) is an interactive on-line learning environment delivering "best of the best," researched-based knowledge from the top minds across the land-grant university system.  It is a space where university content providers can collaborate to gather and produce new educational and information resources on wide-ranging topics while continually interacting with their customers to help solve real-life problems in real time.  The works of faculty... M. Lambur

5. Bayesian Methods for Predicting LAI and Soil Moisture

Crop models describe the growth and development of a crop interacting with soil, climate, and management... M. Majdi, D. Benjamin, D. Marie-france

6. An Approach to Selection of Soil Water Content Monitoring Locations within Fields

Increased input efficiency is one of the main challenges for a modern agricultural enterprise. One way to optimize production cycles is to rationalize crop residue utilization. In conditions where there is limited use of mineral fertilizers and without applying manure, plant residues may be used as an organic fertilizer as... V.I. Adamchuk, L. Pan, R.B. Ferguson

7. Evaluating Different Nitrogen Management Strategies For The Intensive Wheat-Maize System In North China Plain

The sustainable agricultural development involves both environmental challenges and production goals to meet growing food demand. However, excessive nitrogen (N) applications are threatening the sustainability of intensive agriculture in the North China Plain (NCP). Improved N management should result in greater N use efficiency (NUE) and producer profit while reducing the risk of environmental contamination. Therefore, developing and disseminating feasible N management strategies... Q. Cao, Y. Miao, G. Feng, F. Li, B. Liu, X. Gao, Y. Liu

8. Design Of ECU Monitoring System For Agricultural Vehicle Based On ISO 11783

International standard for implementation of electronic control unit (ECU) in agricultural tractors has been requirement for inter-operation compatibility of various agricultural vehicles. The ISO 11783 standard is basically based on  communication technology designated using the controller area network (CAN), it is typical standard technology for implementation of ECU in agricultural vehicle. CAN bus Communication system was developed to the distribution control of ECUs to... W. Yang , S. Kim, J. Moon, D. Kim

9. 3D Map in the Depth Direction of Field for Precision Agriculture

 By a change in eating habits with economic development and the global population growth, we have been faced with the need for increased food production again. In order to solve the food problem in the future, the introduction of agriculture organization is progressing in emerging countries as well as developed countries. However, the occurrence of natural disasters and abnormal weather, which is becoming a worldwide problem at present, is further weakening the crops of farm... H. Umeda, S. Shibusawa, Q. Li, K. Usui, M. Kodaira

10. Using A Potable Spectroradiometer For In-Situ Measurement Of Soil Properties In A Slope Citrus Field

     In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for crop and soil management. However, the spatial variability of soil properties is consider to be high cost and time consuming to characterize using traditional soil analysis method. To achieve cost and time reduction, the potential benefits of in-situ measurement of soil spectra have been recognized.     ... S. Shibusawa, H. Umeda, K. Usui, M. Kodaira, Q. Li

11. Comparison Of The Variable Potassium Fertilization On The Light And Heavy Soils

Introduction. Determination of the spatial variability of the nutrient levels in soil facilitated adaptation of the fertilizer doses to the soluble forms availability. Nowadays, an increasing use of this method of the fertilizer application is observed, with this being associated with both economical and environmental advantages, as well as, with growing assortment of the purpose-built agricultural instrumentation. An accurate determination of the spatial distribution... P. Grocholski, P. Stepien, G. Kulczycki, A. Michalski

12. Selection and Utility of Uncooled Thermal Cameras for Spatial Crop Temperature Measurement Within Precision Agriculture

Since previous research used local, single-point measurements to indicate crop water stress, thermography is presented as a technique capable of measuring spatial temperatures supporting its use for monitoring crop water stress. This study investigated measurement accuracy of uncooled thermal cameras under strict environmental conditions, developed hardware and software to implement uncooled thermal cameras and quantified intrinsic properties that impact measurement accuracy and repeatability.... D.L. Mangus, A. Sharda

13. Considering Farmers' Situated Expertise in AgriDSS Development to Fostering Sustainable Farming Practices in Precision Agriculture

Agriculture is facing immense challenges and sustainable intensification has been presented as a way forward where precision agriculture (PA) plays an important role. More sustainable agriculture needs farmers who embrace situated expertise and can handle changing farming systems. Many agricultural decision support systems (AgriDSS) have been developed to support farm management, but the traditional approach to AgriDSS development is mostly based on knowledge transfer. This has resulted in technology... C. Lundström, J. Lindblom

14. Development of a Multispectral Sensor for Crop Canopy Temperature Measurement

Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One approach... P. Drew, K.A. Sudduth, E. Sadler

15. EZZone - An Online Tool for Delineating Management Zones

Management zones are a pillar of Precision Agriculture research.  Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions.  The use of Geographic Information Systems for agricultural management is common, especially with management zones.  Although many algorithms have been produced in research settings, no online software for management zone delineation exists.  This research used a common... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos

16. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer Vision

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas

17. Helvis - a Small-scale Agricultural Mobile Robot Prototype for Precision Agriculture

The use of agricultural robots is emerging in a complex scenario where it is necessary to produce more food to feed a crescent population, decrease production costs, fight plagues and diseases, and preserve nature. Around the world, there are many research institutes and companies trying to apply mobile robotics techniques in agricultural fields. Mostly, large prototypes are being used and their shapes and dimensions are very similar to tractors and trucks. In the present study, a small-scale... M. Becker, A.E. Velasquez, H.B. Guerrero, V.A. Higuti, D.M. Milori, D.V. Magalhães

18. Challenges and Successes when Generating In-season Multi-temporal Calibrated Aerial Imagery

Digital aerial imagery (DAI) of the crop canopy collected by aircraft and unmanned aerial vehicles is the yardstick of precision agriculture.  However, the quantitative use of this imagery is often limited by its variable characteristics, low quality, and lack of radiometric calibration.  To increase the quality and utility of using DAI in crop management, it is important to evaluate and address these limitations of DAI.  Even though there have been improvements in spatial resolution... P.M. Kyveryga, J. Pritsolas, J. Connor, R. Pearson

19. Integrated Approach to Site-specific Soil Fertility Management

In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a funda­mental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor

20. Within-field Profitability Assessment: Impact of Weather, Field Management and Soils

Profitability in crop production is largely driven by crop yield, production costs and commodity prices. The objective of this study was to quantify the often substantial yet somewhat illusive impact of weather, management, and soil spatial variability on within-field profitability in corn and soybean crop production using profitability indices for profit (net return) and return-on-investment (ROI) to produce estimates. We analyzed yield and cropping system data provided by 42 farmers within Central... P.M. Kyveryga, S. Fey, J. Connor, A. Kiel, D. Muth

21. Developing an Integrated Approach for Estimation of Soil Available Nutrient Content Using the Modified WOFOST Model and Time-Series Multispectral UAV Observations

Soil available nutrient (SAN) plays an important role in crop growth, yield formation, and plant-soil-atmosphere system exchange. Nitrogen (N), phosphorus (P) and potassium (K) are recognized as three primary nutrients in crop production. Accurate and timely information on SAN conditions at key crop growth stages is important for developing beneficial management practices. While traditional field sampling can obtain reliable information for limited number of sites, it is infeasible for spatially... Z. Cheng, J. Meng, J. Shang, J. Liu, B. Qian, Q. Jing

22. Realising the Full Potential of Precision Agriculture: Encouraging Farmer 'Buy-in' by Building Trust in Data Sharing

Uncertainty around the ownership, privacy and security of farm data are most commonly the reasons cited for farmer’s reluctance to “buy-in” to big data in agriculture. Evidence provided to the recent US Committee on Commerce, Science, and Transportation Subcommittee on Consumer Protections, Product Safety, Insurance, and Data Security, United States Senate Technology in Agriculture: Data Driven Farming (Nov 2017) highlighted that “data ownership, and related... L. Wiseman, J. Sanderson

23. Toward a Precision Agricultural Implementation for Sugar Cane Plantations in Southwestern Region of Colombia, South America

The Colombian Sugar Cane Research Center, CENICAÑA, has initiated an ambitious project for the implementation of Precision Agriculture (PA) technologies in the Cauca river valley region, where one of its main objectives is to have the ability to collect large volumes of geospatial data. The main sugarcane growers in the country perform their work in the selected work area, which covers an area of ​​approximately 242,000 ha, characterized by diverse topographic and edaphic conditions.... J.A. Celades, J.H. Caicedo, C.E. García, H. Mora

24. Design of Ground Surface Sensing Using RADAR

Ground sensing is the key task in harvesting head control system. Real time sensing of field topography under vegetation canopy is very challenging task in wild blueberry cropping system. This paper presents the design of an ultra-wide band RADAR sensing, scanning device to recognize the soil surface level under the canopy structure. Requirements for software and hardware were considered to determine the usability of the ultra-wide band RADAR system.An automated head elevation... M.M. Mohamed, Q. Zaman, T. Esau, A. Farooque

25. Survey Shows Specialty and Commodity Crop Retailers Use Precision Agriculture Differently

The 2021 CropLife-Purdue Survey of precision agricultural practices by US agricultural input dealers serving the American grain and oilseed sector shows that most of them use GPS guidance and related technologies like sprayer boom control, most provide variable rate fertilizer services, and the majority say that fertilizer decisions are influenced by grower data. In contrast, dealers serving horticultural and specialty crop farms indicate comparatively modest adoption of many precision agriculture... B.J. Erickson, J. Lowenberg-deboer

26. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep Learning

Unmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniques... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun

27. Where to Put Treatments for On-farm Experimentation

On-farm experimentation has become more and more popular due to advancements in technology. These experiments are not as costly as before, as current machinery can allocate different levels of treatment to specific plots. The main goal of this kind of experiment is to obtain a site-specific nutrient level. The yield behavior is different based on the researcher’s treatment. One unanswered question for on-farm experimentation is how the treatments should be allocated in the first place such... D. Poursina, W. Brorsen

28. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and Indiana

Precision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor

29. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone Delineation

Management zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve

30. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS Receivers

GNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performed,... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic

31. The ISO Strategic Advisory Group for Smart Farming: a Multi-pronged Opportunity for Greater Global Interoperability

Agriculture is becoming increasingly complex and producers must secure their profitability, sustainability, and freedom to operate under a progressively more challenging set of constraints such as climate change, regulatory pressure, changes in consumer preferences, increasing cost of inputs, and commodity price volatility. We have not, however, yet reached the level of data interoperability required for a truly "smart" farming that can tackle the aforementioned problems... R. Ferreyra, J. Lehmann