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Kanannnavar, P.S
Kremer, R
Kakarla, S
Klein, R.N
Karkee, M
Zhang, Y
Zimba, P.V
Zamora, M
Kitchen, N.R
Kepka, M
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Authors
Shanwad, U
H, V
N.L., R
Kanannnavar, P.S
Swamy, S
Patil, M.B
Kitchen, N.R
Sudduth, K.A
Myers, D.B
Kremer, R.J
Kitchen, N.R
Sudduth, K.A
Myers, D.B
Klein, R.N
Golus, J.A
Roberts, D.F
Shanahan, J.F
Fergugson, R.B
Adamchuk, V.I
Kitchen, N.R
Ahamed, T
Tian, L
Zhang, Y
Xiong, Y
Zhao, B
Jiang, Y
Ting, K
Sheridan, A
Sudduth, K.A
Kitchen, N.R
Kitchen, N.R
Suddth, K.S
Drummond, S.T
Allphin, E
Kitchen, N.R
Suddeth, K.A
Thompson, A
Sudduth, K.A
Kitchen, N.R
Drummond, S.T
Myers, D.B
Kitchen, N.R
Sudduth, K.A
Leonard, B.J
De Kleine, M
Karkee, M
Zhang, Q
Lewis, K
Baffaut, C
Sudduth, K
Sadler, J
Kremer, R
Lerch, R
Kitchen, N
Veum, K
Karkee, M
Zhang, Q
Sharda, A
Charvat, K
Reznik, T
Charvat jr., K
Lukas, V
Horakova, S
Kepka, M
Charvat, K
Reznik, T
Lukas, V
Charvat Jr., K
Horakova, S
Splichal, M
Kepka, M
Skouby, D
Schumacher, L
Yost, M
Kitchen, N.R
Thomson, S.J
DeFauw, S.L
English, P.J
Hanks, J.E
Fisher, D.K
Foster, P.N
Zimba, P.V
Yost, M.A
Kitchen, N.R
Sudduth, K.A
Drummond, S.T
Massey, R.E
Bhusal, S
Khanal, K
Karkee, M
Steensma, K.M
Taylor, M.E
Bean, G.M
Kitchen, N.R
Camberato, J.J
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Sawyer, J.E
Scharf, P.C
Ransom, C.J
Kitchen, N.R
Camberato, J.J
Carter, P.R
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Shanahan, J
Sawyer, J.E
Kitchen, N.R
Yost, M.A
Ransom, C.J
Bean, G
Camberato, J
Carter, P
Ferguson, R
Fernandez, F
Franzen, D
Laboski, C
Nafziger, E
Sawyer, J
Dukes, M
Zamora, M
Rowland, D
Ampatzidis, Y
Derival, M
Kakarla, S
Albrecht, U
Zhang, X
Charvat, K
Kepka, M
Berzins, R
Zadrazil, F
Langovskis, D
Musil, M
Kitchen, N.R
Ransom, C.J
Schepters, J.S
Hatfield, J.L
Massey, R
Conway, L.S
Vong, C
Kitchen, N.R
Sudduth, K.A
Anderson, S.H
Sudduth, K.A
Kitchen, N.R
Conway, L.S
Upadhyaya, P
Karkee, M
Zhang, X
Kashetri, S
Kang, C
Karkee, M
Zhang, Q
Shcherbatyuk, N
Davadant, P
Keller, M
Topics
Precision Nutrient Management
Information Management and Traceability
Proximal Sensing in Precision Agriculture
Guidance, Auto Steer, and GPS Systems
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Sensor Application in Managing In-season Crop Variability
Spatial Variability in Crop, Soil and Natural Resources
Spatial Variability in Crop, Soil and Natural Resources
Engineering Technologies and Advances
Precision Conservation Management
Precision Crop Protection
Standards & Data Stewardship
Precision Agriculture and Climate Change
Agricultural Education
Remote Sensing Application / Sensor Technology
Profitability and Success Stories in Precision Agriculture
Applications of Unmanned Aerial Systems
In-Season Nitrogen Management
Drainage Optimization and Variable Rate Irrigation
Precision Horticulture
Drainage Optimization and Variable Rate Irrigation
In-Season Nitrogen Management
Site-Specific Nutrient, Lime and Seed Management
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Poster
Oral
Year
2012
2010
2014
2016
2008
2018
2022
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Authors

Filter results31 paper(s) found.

1. Using GPS-RTK In Crop Variety And Hybrid Evaluations

The traditional methods used by many to conduct research in crop variety and hybrid evaluations is to blank plant the area, flag the area, or use a physical marker. All of these have disadvantages. In blank planting it may be difficult to plant exactly in the same rows, and can dry the soil and affect seed germination if soil water is limited. Blank planting also destroys crop residues and with skip-row residues are destroyed in the unplanted rows.This method is used for many plots in cooperator’s... R.N. Klein, J.A. Golus

2. A Crop And Soil Strategy For Sensor-based Variable-rate Nitrogen Management

Crop-based active canopy sensors and soil-based management zones (MZ) are currently being studied as tools to direct in-season variable-rate N application. Some have suggested the integration of these tools as a more robust decision tool for guiding spatially variable N rates. The objectives of this study were to identify (1) soil variables useful for MZ delineation and (2) determine if MZ could be useful in identifying field areas with... D.F. Roberts, J.F. Shanahan, R.B. Fergugson, V.I. Adamchuk, N.R. Kitchen

3. Site-specific Management For Biomass Feedstock Production: Development Of Remote Sensing Data Acquisition Systems

Efficient biomass feedstock production supply chain spans from site-specific management of crops on field to the gate of biorefinery. Remote sensing data acquisition systems have been introduced for site-specific management, which is a part of the engineering solutions for biomass feedstock production. A stand alone tower remote sensing platform was developed to monitor energy crops using multispectral imagery. The sensing system was capable of collecting RGB and CIR images during the crop growing... T. Ahamed, L. Tian, Y. Zhang, Y. Xiong, B. Zhao, Y. Jiang, K. Ting

4. Canopy Reflectance Sensing As Impacted By Corn Hybrid Growth

  Detection of physical and chemical properties within the growing season could help predict the overall health and yield of a corn crop. Little research has been done to show differences of corn hybrids on canopy reflectance sensing. This study was conducted to examine these potential differences during the early- to mid-vegetative growth stages of corn on three different soil types in Missouri. Canopy sensing (Crop Circle) and SPAD chlorophyll meter... A. Sheridan, K.A. Sudduth, N.R. Kitchen

5. Is A Nitrogen-rich Reference Needed For Canopy Sensor-based Corn Nitrogen Applications?

The nitrogen (N) supplying capacity of the soil available to support corn (Zea mays L.) production can be highly variable both among and within fields. In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop N health and fertilization. Typically the procedure followed compares the crop in an area known to be non-limiting in N (called a N-rich area) to the crop in areas inadequately fertilized. Measurements from the two areas are used to calculate... N.R. Kitchen, K.S. Suddth, S.T. Drummond

6. Nitrogen Loss In Corn Production Varies As A Function Of Topsoil Depth

  Understanding availability and loss potential of nitrogen for varying topsoil depths of poorly-drained claypan soil landscapes could help producers make improve decisions when managing crops for feed grain or bio-fuels.  While it has been well documented that topsoil depth on these soils plays an important role in storing water for crop growth, it is not well known how this same soil... E. Allphin, N.R. Kitchen, K.A. Suddeth, A. Thompson

7. Comparison Of Three Canopy Reflectance Sensors For Variable-rate Nitrogen Application In Corn

In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop nitrogen (N) health and subsequent control of N fertilization. The several sensor systems that are now commercially available have design and operational differences. One difference is the sensed wavelengths, although these typically include wavelengths in both the visible and near-infrared ranges. Another difference is orientation – the sensors most commonly used in the US are designed to... K.A. Sudduth, N.R. Kitchen, S.T. Drummond

8. Precision Nutrient Management in Cotton- A Case Study from India

Cotton is being one of the important commercial crops in India, farmers have adopted cultivating hybrid cotton to achieve higher yield. In this context, cotton is becoming input intensive crop... U. Shanwad, V. H, R. N.l., P.S. Kanannnavar, S. Swamy, M.B. Patil

9. Issues in Analysis of Soil-Landscape Effects in a Large Regional Yield Map Collection

     Yield maps are commonly collected by producers and precision-agriculture service providers and are accumulating in warehouse scale data-stores. A key goal in analysis of yield maps is to understand how climate interacts with soil landscapes to cause spatial and temporal variability in grain yield. However, there are many issues that limit utilization of yield map data for this purpose including: i) yield-landscape inversion between climate years,... N.R. Kitchen, K.A. Sudduth, D.B. Myers

10. Estimating Soil Quality Indicators with Diffuse Reflectance Spectroscopy

Knowledge of within-field spatial variability in soil quality indicators is important to assess the impact of site-specific management on the soil. Standard methods for measuring these properties require considerable time and expense, so sensor-based approaches would be... R.J. Kremer, N.R. Kitchen, K.A. Sudduth, D.B. Myers

11. Physiological Repsonses Of Corn To Variable Seeding Rates In Landscape-Scale Strip Trials

Many producers now have the capability to vary seeding rates on-the-go. Methods are needed to develop variable rate seeding approaches in corn but require an understanding of the physiological response of corn to soil-landscape and weather conditions. Interplant competition fundamentally differs at varied seeding rate and may affect corn leaf area, transpiration, plant morphology, and assimilate partitioning. Optimizing these physiological effects with optimal seeding rates in a site-specific... D.B. Myers, N.R. Kitchen, K.A. Sudduth, B.J. Leonard

12. A Dual Motor Actuator Used To Detach Fruit By Shaking Limbs Of Fruit Trees

Mechanizing the fruit removal operation during fresh-market apple harvesting will result in considerable cost savings for fruit growers. This study introduces a mechanical fruit removal technique that uses a unique limb shaking mechanism called a Dual Motor Actuator (DMA). The DMA was developed as an infinitely variable end-effector that applies rhythmic motions to a fruiting limb to remove fruit. The novelty of the DMA design is the use of two eccentrics mounted to electric motors... M. De kleine, M. Karkee, Q. Zhang, K. Lewis

13. Production And Conservation Results From A Decade-Long Field-Scale Precision Agriculture System

Research is needed that simultaneously evaluates production and conservation outcomes of precision agriculture practices.  From over a decade (1993-2003) of yield and soil mapping and water quality assessment, a multi-faceted, “precision agriculture system” (PAS) was developed and initiated in 2004 on a 36-ha field in Central Missouri. The PAS assessment was accomplished by comparing it to the previous decade of conventional corn-soybean... C. Baffaut, K. Sudduth, J. Sadler, R. Kremer, R. Lerch, N. Kitchen, K. Veum

14. Effect Of Time Of Application On Spray Coverage Using Solid Set Canopy Delivery System

Permanent or solid set canopy delivery system can be used for foliar application in tree fruit orchards. The emitters are placed along the tree rows and are very close to tree canopy. During spray application droplets quickly get deposited on tree canopy and coverage of up to 90% could be achieved. However concerns still exist regarding critical time required to achieve target coverage using SSCD system. This knowledge of selecting an appropriate application time could help growers... M. Karkee, Q. Zhang, A. Sharda

15. FOODIE Data Model for Precision Agriculture

The agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production. The FOODIE service platform deals with including their thematic, spatial, and temporal... K. Charvat, T. Reznik, K. Charvat jr., V. Lukas, S. Horakova, M. Kepka

16. Quo Vadis Precision Farming

The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenced... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka

17. A Content Review of Precision Agriculture Courses Across the US

Knowledge of what precision agriculture (PA) content is currently taught across the United States will help build a better understanding for what PA instructors should incorporate into their classes in the future. The University of Missouri partnered with several universities throughout the nation on a USDA challenge grant. Precision Agriculture faculty from 24 colleges/universities from across the U.S. shared their PA content by sharing their syllabi from 43 different courses. The syllabi were... D. Skouby, L. Schumacher, M. Yost, N.R. Kitchen

18. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)

Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba

19. A Long-Term Precision Agriculture System Maintains Profitability

After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or ‘business-as-usual’ system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36-ha field in central Missouri during 1993 to 2003. Following this, a ‘precision agriculture... M.A. Yost, N.R. Kitchen, K.A. Sudduth, S.T. Drummond, R.E. Massey

20. Unmanned Aerial Systems (UAS) for Mitigating Bird Damage in Wine Grapes

Bird predation is a significant problem in high-value fruit crops, such as apples, cherries, blueberries, and wine grapes. Conventional methods such as netting, falconry, auditory scaring devices, lethal shooting, and visual scare devices are reported to be ineffective, costly, and/or difficult to manage. Therefore, farmers are in need of more effective and affordable bird control methods. In this study, two UAS wasused as a bird-deterring agent in a commercial vineyard. The experimental... S. Bhusal, K. Khanal, M. Karkee, K.M. Steensma, M.E. Taylor

21. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen Rates

Nitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf

22. Improving Corn Nitrogen Rate Recommendations Through Tool Fusion

 Improving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation was... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer

23. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer Response

Improving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three growing... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer

24. Effect of Irrigation Scheduling Technique and Fertility Level on Corn Yield and Nitrogen Movement

Florida has more first magnitude springs that anywhere in the world. Most of these are located in north Florida where agricultural production is the primary basis for the economy. Irrigated corn has become a popular part of the crop rotation in recent years. This project is a study of a corn and peanut rotation investigating Best Management Practices (BMPs) of nitrogen fertility level (336, 246, 157 kg/ha) and irrigation strategies as follows:  (i) GROW, mimicking grower’s practices,... M. Dukes, M. Zamora, D. Rowland

25. Evaluation of HLB-Infected Citrus Rootstocks Using Ground Penetrating Radar

Citrus production in Florida continues to decline steadily, since the arrival of Huanglongbing (HLB or citrus greening). HLB does not kill the tree, but HLB-infected trees become less productive. Since now, there is no cure for this disease. However, several strategies have been developed to manage and control HLB-infected citrus trees. We have developed and evaluated a heat thermotherapy system (short-term solution) for sustaining productivity of HLB-affected trees. This system heats the canopy... Y. Ampatzidis, M. Derival, S. Kakarla, U. Albrecht, X. Zhang

26. SmartAgriHubs FIE20 - Groundwater and Meteo Sensors and Earth Observation for Precision Agriculture

The solution developed under the SmartAgriHubs project in the scope of the Flagship Innovation Experiment FIE20 Groundwater and meteo sensors is an expert system to support farmers in decision-making process and planning process of field interventions. This FIE20 solution integrates various data sources and different analytical processes in a complete system and provides users an easy-to-use web map application as a common user interface. The FIE20 system integrates components developed during... K. Charvat, M. Kepka, R. Berzins, F. Zadrazil, D. Langovskis, M. Musil

27. Spatial and Temporal Factors Impacting Incremental Corn Nitrogen Fertilier Use Efficiency

Current tools for making crop N fertilizer recommendations are primarily based on plot and field studies that relate the recommendation to the economic optional N rate (EONR).  Some tools rely entirely on localized EONR (e.g., MRTN). In recent years, tools have been developed or adapted to  account for within-field variation in crop N need or variable within season factors. Separately, attention continues to elevate for how N fertilizer recommendations might account for environmental... N.R. Kitchen, C.J. Ransom, J.S. Schepters, J.L. Hatfield, R. Massey

28. Predicting Corn Emergence Uniformity with On-the-go Furrow Sensing Technology

Integration of proximal soil sensors into commercial row-crop planter components have allowed for a dense quantification of within-field soil spatial variability. These technologies have potential to guide real-time management decisions, such as on-the-go variable seeding rate or depth. However, little is known about the performance of these systems. Therefore, research was conducted in central Missouri, USA to determine the relationship between planter sensor metrics, and corn (Zea mays L.) ... L.S. Conway, C. Vong, N.R. Kitchen, K.A. Sudduth, S.H. Anderson

29. Soil, Landscape, and Weather Affect Spatial Distributions of Corn Population and Yield

As more planters are equipped with the technology to vary seeding rate, evaluation of the within-field relationships between plant stand density (or population) and yield is needed. One aspect of this evaluation is determining how stand loss and yield are related to soil and landscape factors, and how these relationships vary with different weather conditions. Therefore, this research examined nine site-years of mapped corn yield, harvest population, and soil and landscape data obtained for a... K.A. Sudduth, N.R. Kitchen, L.S. Conway

30. Automated Lag Phase Detection in Wine Grapes

Crop yield estimation, an important managerial tool for vineyard managers, plays a crucial role in planning pre/post-harvest operations to achieve desired yield and improve efficiency of various field operations. Although various technological approaches have been developed in the past for automated yield estimation in wine grapes, challenges such as cost and complexity of the technology, need of higher technical expertise for their operation and insufficient accuracy have caused major concerns... P. Upadhyaya, M. Karkee, X. Zhang, S. Kashetri

31. Diagnosis of Grapevine Nutrient Content Using Proximal Hyperspectral Imaging

Nutrient deficiencies on grapevines could affect the fruit yield and quality, which is a major concern in vineyards. Nutrient deficiencies may be recognizable by foliar symptoms that vary by mineral nutrient and stress severity, but it is too late to manage when visible deficiency symptoms become apparent. The nutrient analysis in the laboratory is the way to get an accurate result, but it is time and cost-intensive. The differences in leaf nutrient levels also alter spectral characteristics outside... C. Kang, M. Karkee, Q. Zhang, N. Shcherbatyuk, P. Davadant, M. Keller