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Jia, M
Zaman, Q
Znoj, E
Ko-Madden, C
Kitchen, N
Zhao, L
Kotlyarov , D
Zhai, C
Kirkpatrick, T
Kshetri, S
King, W
Karstoft, H
Johnson, A
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Authors
Sun, C
Ji, Z
Qian, J
Li, M
Zhao, L
Li, W
Zhou, C
Du, X
Xie, J
Wu, T
Qu, L
Hao, L
Yang, X
Pullanagari, R
Yule, I
Tuohy, M
Hedley, M
King, W
Dynes, R
Zaman, Q
Schumann, A.W
Percival, D.C
Esau, T.J
Read, S.M
Khalilian, A
Henderson, W
Mueller, J
Kirkpatrick, T
Monfort, S
Overstreet, C
Farooque, A.A
Zaman, Q
Schumann, A.W
Percival, D.C
Esau, T.J
Stauffer, T
Baffaut, C
Sudduth, K
Sadler, J
Kremer, R
Lerch, R
Kitchen, N
Veum, K
Thompson, A
Boardman, D.L
Kitchen, N
Allphin, E
Kizer, E
Upadhyaya, S.K
Rojo, F
Ozmen, S
Ko-Madden, C
Zhang, Q
Weckler, P
Wang, N
Zhai, C
Zhang, L
Luo, B
Long, J
Taylor, R
Sedinina, N
Kotlyarov , D
Kotlyarov, V
Nowatzki, J
Bajwa, S
Roberts, D
Ossowski, M
Scheve, A
Johnson, A
Chaplin, Y
Veum, K
Sudduth, K
Kitchen, N
Yost, M.A
Kitchen, N
Sudduth, K
Drummond, S
Sadler, J
Conway, L
Yost, M
Kitchen, N
Sudduth, K
Myers, B
Ransom, C.J
Bean, M
Kitchen, N
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Bobryk, C.W
Yost, M
Kitchen, N
Scharf, P
Shannon, K
Sudduth, K
Kitchen, N
Skovsen, S
Dyrmann, M
Eriksen, J
Gislum, R
Karstoft, H
Jørgensen, R.N
King, W
Dynes, R
Laurenson, S
Zydenbos, S
MacAuliffe, R
Taylor, A
Manning, M
Roberts, A
White, M
Kross, A
Kaur, G
Znoj, E
Callegari, D
Sunohara, M
McNairn, H
Lapen, D
Rudy, H
van Vliet, L
He, Z
Manoj, K
Zhang, Q
Kshetri, S
Lu, J
Chen, Z
Miao, Y
Li, Y
Zhang, Y
Zhao, X
Jia, M
Topics
Information Management and Traceability
Proximal Sensing in Precision Agriculture
Engineering Technologies and Advances
Spatial Variability in Crop, Soil and Natural Resources
Precision Conservation Management
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Engineering Technologies and Advances
Precision Crop Protection
Unmanned Aerial Systems
Precision Conservation Management
Spatial Variability in Crop, Soil and Natural Resources
Sensor Application in Managing In-season Crop Variability
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Site-Specific Pasture Management
Big Data, Data Mining and Deep Learning
In-Season Nitrogen Management
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
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Authors

Filter results22 paper(s) found.

1. Performance Evaluation Of A Prototype Variable Rate Sprayer For Spot- Application Of Agrochemicals In Wild Blueberry Fields

  Wild blueberry yields are highly dependent on agrochemicals for adequate weed control. The excessive use of agrochemicals with uniform application in significant bare spots and plant areas has resulted in increased cost of production. A cost-effective automated prototype variable rate (VR) sprayer was developed for spot-application (SA) of agrochemicals in a specific section of the sprayer boom where the weeds have been detected. The weed patches were mapped with an RTK-GPS... Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, S.M. Read

2. Development Of A System For Site-specific Nematicide Placement In Cotton

Nematode distribution varies significantly in cotton fields. Population density throughout a field is highly correlated to soil texture. Field-wide application of a uniform nematicide rate results in the chemical being applied to areas without nematodes or where nematode densities are below an economic threshold, or the application of sub-effective levels in areas with high nematode densities. The investigators have developed a “Site- Specific Nematicide Placement”... A. Khalilian, W. Henderson, J. Mueller, T. Kirkpatrick, S. Monfort, C. Overstreet

3. Estimating Soil Moisture And Organic Matter Content Variabality Using Electromagnatic Induction Metod

  Abstract: Electromagnetic induction (EMI) methods are gaining popularity due to their non-destructive nature, rapid response and ease of integration into mobile platforms for assessment of the soil moisture content, water table depth, and salinity etc. The objective of this study was to estimate and map soil moisture content and organic matter content using DualEM.... A. Farooque, Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, T. Stauffer

4. Towards a Multi-Source Record Keeping System for Agricultural Product Traceability

Agricultural production record keeping is the basis of traceability system. To resolve the problem including single method of information acquisition, weak ability of real-time monitoring and low credibility of history information in agricultural production process, the... C. Sun, Z. Ji, J. Qian, M. Li, L. Zhao, W. Li, C. Zhou, X. Du, J. Xie, T. Wu, L. Qu, L. Hao, X. Yang

5. Proximal Sensing Tools to Estimate Pasture Quality Parameters.

To date systems for estimating pasture quality have relied on destructive sampling with measurement completed in a laboratory which was very time consuming and expensive. Results were often not received until after the pasture was grazed which defeated the point of the measurement, as farmers required the information to make decisions about grazing strategies to effectively... R. Pullanagari, I. Yule, M. Tuohy, M. Hedley, W. King, . Dynes

6. 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

7. Water And Nitrogen Use Efficiency Of Corn And Switchgrass On Claypan Soil Landscapes

Claypan soils cover a significant portion of Missouri and Illinois crop land, approximately 4 million ha. Claypan soils, characterized with a pronounced argilic horizon at or below the soil surface, can restrict nutrient availability and uptake, plant water storage, and water infiltration. These soil characteristics affect plant growth, with increasing depth of the topsoil above the claypan horizon having a strong positive correlation to grain crop production. In the case of low... A. Thompson, D.L. Boardman, N. Kitchen, E. Allphin

8. Proximal Sensing of Leaf Temperature and Microclimatic Variables to Implement Precision Irrigation in Almond and Grape Crops

Irrigation decisions based on traditional soil moisture sensing often leads to uncertainty regarding the true amount of water available to the plant. Plant based sensing of water stress decreases this uncertainty. In specialty crops grown in California’s Central Valley, precision deficit irrigation based on plant water stress could be used to decrease water use and increase water use efficiency by supplying the necessary quantity of water only when it is needed by the plant. However, there... E. Kizer, S.K. Upadhyaya, F. Rojo, S. Ozmen, C. Ko-madden, Q. Zhang

9. Evaluation of a Seed-fertilizer Application System Using a Laser Scanner

The system evaluated is a design that combines planter and sprayer technologies to allow clients to plant crops while simultaneously spraying initial fertilizer on or in close proximity to the seed.  The system is an idea Capstan Ag Systems has been pursuing for around 15 years, and has recently been revived in a partnership with Great Plains Manufacturing Company.  Great Plains Manufacturing released the final product under the name AccushotTM at the 2015... P. Weckler, N. Wang, C. Zhai, L. Zhang, B. Luo, J. Long, R. Taylor

10. New Technologies in Biological Plant Protection and Its Localization

The sharp increase in the use of pesticides in agrobiocenosis in the background of no-till and minimum tillage called: the growth of costs, the decline of soil fertility, the occurrence of resistance in harmful organisms and change in species composition, a number of other pressing environmental problems. In this regard, the most preferred and safe bipolarization of plant protection. The use of microorganisms in plant protection can reduce the number of harmful organisms in anthropogenic ecosystems,... N. Sedinina, D. Kotlyarov , V. kotlyarov

11. Large-scale UAS Data Collection, Processing and Management for Field Crop Management

North Dakota State University research and Extension personnel are collaborating with Elbit Systems of America to compare the usefulness and economics of imagery collected from a large unmanned aircraft systems (UAS), small UAS and satellite imagery. Project personnel are using a large UAS powered with an internal combustion engine to collect high-resolution imagery over 100,000 acres twice each month during the crop growing season. Four-band multispectral Imagery is also being collected twice... J. Nowatzki, S. Bajwa, D. Roberts, M. Ossowski, A. Scheve, A. Johnson, Y. Chaplin

12. Sensor Based Soil Health Assessment

Quantification and assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region, visible, near-infrared (VNIR)... K. Veum, K. Sudduth, N. Kitchen

13. A Decade of Precision Agriculture Impacts on Grain Yield and Yield Variation

Targeting management practices and inputs with precision agriculture has high potential to meet some of the grand challenges of sustainability in the coming century, including simultaneously improving crop yields and reducing environmental impacts. Although the potential is high, few studies have documented long-term effects of precision agriculture on crop production and environmental quality. More specifically, long-term impacts of precision conservation practices such as cover crops, no-tillage,... M.A. Yost, N. Kitchen, K. Sudduth, S. Drummond, J. Sadler

14. Claypan Depth Effect on Soil Phosphorus and Potassium Dynamics

Understanding the effects of fertilizer addition and crop removal on long-term change in spatially-variable soil test P (STP) and soil test K (STK) is crucial for maximizing the use of grower inputs on claypan soils. Using apparent electrical conductivity (ECa) to estimate topsoil depth (or depth to claypan, DTC) within fields could help capture the variability and guide site-specific applications of P and K. The objective of this study was to determine if DTC derived from ECa... L. Conway, M. Yost, N. Kitchen, K. Sudduth, B. Myers

15. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor Algorithm

Nitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

16. Field Potential Soil Variability Index to Identify Precision Agriculture Opportunity

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These concerns... C.W. Bobryk, M. Yost, N. Kitchen

17. Sensor-based Nitrogen Applications Out-performed Producer-chosen Rates for Corn in On-farm Demonstrations

Optimal nitrogen fertilizer rate for corn can vary substantially within and among fields.  Current N management practices do not address this variability.  Crop reflectance sensors offer the potential to diagnose crop N need and control N application rates at a fine spatial scale.  Our objective was to evaluate the performance of sensor-based variable-rate N applications to corn, relative to constant N rates chosen by the producer.  Fifty-five replicated on-farm demonstrations... P. Scharf, K. Shannon, K. Sudduth, N. Kitchen

18. Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-Based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds

Targeted fertilization of grass clover leys shows high financial and environmental potentials leading to higher yields of increased quality, while reducing nitrate leaching. To realize the gains, an accurate fertilization map is required, which is closely related to the local composition of plant species in the biomass. In our setup, we utilize a top-down canopy view of the grass clover ley to estimate the composition of the vegetation, and predict the composition of the dry matter of the forage.... S. Skovsen, M. Dyrmann, J. Eriksen, R. Gislum, H. Karstoft, R.N. Jørgensen

19. Through the Grass Ceiling: Using Multiple Data Sources on Intra-Field Variability to Reset Expectations of Pasture Production and Farm Profitability

Intra-field variability has received much attention in arable and horticultural contexts. It has resulted in increased profitability as well as reduced environmental footprint. However, in a pastoral context, the value of understanding intra-field variability has not been widely appreciated. In this programme, we used available technologies to develop multiple data layers on multiple fields within a dairy farm. This farm was selected as it was already performing at a high level, with well-developed... W. King, R. Dynes, S. Laurenson, S. Zydenbos, R. Macauliffe, A. Taylor, M. Manning, A. Roberts, M. White

20. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield

The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet

21. Real-time Detection of Picking Region of Ridge Planted Strawberries Based on YOLOv5s with a Modified Neck

Robotic strawberry harvesting requires machine vision system to have the ability to detect the presence, maturity, and location of strawberries. Strawberries, however, can easily be bruised, injured, and even damaged during robotic harvest if not picked properly because of their soft surfaces. Therefore, it is important to cut or pick the strawberry stems instead of picking the fruit directly. Additionally, real-time detection is critical for robotic strawberry harvesting to adapt to the changing... Z. He, K. Manoj, Q. Zhang, S. Kshetri

22. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia