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Nascimento-Silva, K
Gidea, M
Dong, J
Berry, P
Westfall, D.G
Griffin, T.W
Boini, A
Hunsche, M
Westfall, D
Yilma, W
Bellenguez, R
White, S.N
Hachisuca, A.
Yan, N
Gebbers, R
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Authors
Khosla, R
Westfall, D.G
Longchamps, L
Longchamps, L
Panneton, B
Westfall, D.G
Khosla, R
Betz, A
Benny, H
Jens, M
Özyurtlu, M
Pflanz, M
Rachow-Autrum, T
Schischmanow, A
Scheele, M
Schrenk, J
Schrenk, L
Zude, M
Gebbers, R
Moshia, M.E
Khosla, R
Davis, J
Westfall, D
R, C
Rumpf, T
B, K
Hunsche, M
Pl, L
Noga, G
Shaver, T
Khosla, R
Westfall, D
Peterson, G
Westfall, D
Sherrod, L.A
McNeill, D
Bishop-Hurley, G.J
Irvine, L
Freeman, M
Bellenguez, R
Griffin, T.W
Mark, T
Zeng, H
Wu, B
Yan, N
Leufen, G
Noga, G
Hunsche, M
Leufen, G
Noga, G
Hunsche, M
Wang, C
Chen, T
Dong, J
Li, C
Rothrock, C.S
Monfort, W.S
Griffin, T.W
Spurlock, T.N
Maurer, J.L
Griffin, T.W
Sharda, A
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
Bennett, J
Wilson, C
Sharda, A
Griffin, T.W
Sharda, A
Badua, S
Flippo, D
Ciampitti, I
Griffin, T.W
Musetescu, L
Gidea, M
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Sylvester-Bradley, R
Kindred, D
Berry, P
Sharda, A
Badua, S
Ciampitti, I
Strasser, R
Griffin, T.W
Dhoubhadel, S
Griffin, T.W
Bazzi, C.L
Martins, M.R
Gebler, L
Souza, E.G
Schenatto, K
Sobjak, R
Hachisuca, A.
Franz, F
Peña, J
Melgar, J
de Castro, A
Maja, J
Nascimento-Silva, K
Hennessy, P.J
Esau, T.J
Schumann, A.W
Farooque, A.A
Zaman, Q.U
White, S.N
Siegfried, J
Khosla, R
Mandal, D
Yilma, W
Topics
Precision Nutrient Management
Guidance, Robotics, Automation, and GPS Systems
Precision Horticulture
Precision Nutrient Management
Modeling and Geo-statistics
Sensor Application in Managing In-season Crop Variability
Precision Carbon Management
Precision Livestock Management
Profitability, Sustainability and Adoption
Spatial Variability in Crop, Soil and Natural Resources
Fluorescence Sensing for Precision Crop Management
Proximal Sensing in Precision Agriculture
Precision Crop Protection
Profitability, Sustainability and Adoption
Precision Nutrient Management
Engineering Technologies and Advances
Spatial and Temporal Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Precision Agriculture and Global Food Security
On Farm Experimentation with Site-Specific Technologies
Profitability and Success Stories in Precision Agriculture
Decision Support Systems
Applications of Unmanned Aerial Systems
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2010
2014
2016
2008
2018
2022
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Filter results27 paper(s) found.

1. Precision Manure Management: It Matters Where You Put Your Manure

“Precision fertilizer management” has been around for more than a decade and is practiced widely in Colorado and elsewhere. By precision, we mean application of fertilizer at the right time, in the right place, and in the right amount. However, “Precision Manure Management” is a relatively new concept that converge the best manure management practices with precision nutrient management practices, such as variable rate nutrient application across site-specific management... M.E. Moshia, R. Khosla, J. Davis, D. Westfall

2. Early Identification Of Leaf Rust On Wheat Leaves With Robust Fitting Of Hyperspectral Signatures

Early recognition of pathogen infection is of great relevance in precision plant protection. Disease detection before the occurrence of visual symptoms is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust infected wheat leaves of the susceptible cv. Ritmo 2-4 days after inoculation under controlled conditions. In order to evaluate disease impact on spectral characteristics 215 wavelengths in the range of 370-800... C. R, T. Rumpf, K. B, M. Hunsche, L. Pl, G. Noga

3. Development Of A Nitrogen Requirement Algorithm Using Ground-based Active Remote Sensors In Irrigated Maize

Studies have shown that normalized difference vegetation index (NDVI) from ground-based active remote sensors is highly related with leaf N content in maize (Zea mays). Remotely sensed NDVI imagery can provide valuable information about in-field N variability in maize and significant linear relationships between sensor NDVI and maize grain yield have been found suggesting that an N recommendation algorithm based on NDVI could optimize N application. Therefore, a study was conducted using the two... T. Shaver, R. Khosla, D. Westfall

4. Landscape Position And Climatic Gradient Impacts On Carbon Turnover in Dryland Cropping Systems in Colorado

  Soil organic carbon has decreased in cultivated wheat-fallow systems due to increased carbon oxidation, low carbon input and soil erosion.  Implementation of more intensive cropping with no-till management has reversed the trend in soil carbon loss.  Our objective in this presentation is to review the effects of landscape position on soil carbon status as related to intensification of cropping system.  Our analysis will... G. Peterson, D. Westfall, L.A. Sherrod

5. A Preliminary Evaluation Of Proximity Loggers To Detect Oestrus Behaviour In Grazing Dairy Cows

... D. Mcneill, G.J. Bishop-hurley, L. Irvine, M. Freeman, R. Bellenguez

6. Early Detection of Corn N-Deficiency by Active Fluorescence Sensing in Maize

Globally, the agricultural nitrogen use efficiency (NUE) is no more than 40 %. This low efficiency comes with an agronomic, economic and environmental cost. By better management of spatial and temporal variability of crop nitrogen need, NUE can be improved. Currently available crop canopy sensors based on reflectance are capable... R. Khosla, D.G. Westfall, L. Longchamps

7. Testing The Author Sequence - Finalize

This is just a test to verify the bug with the authors sequence. ... L. Longchamps, B. Panneton, D.G. Westfall, R. Khosla

8. OptiThin - Precision Fruiticulture by Tree-Specific Mechanical Thinning

Apple cultivars show biennial fluctuations in yields (alternate bearing). The phenomenon is induced by reduced yields in one year due to freeze damage, low pollination rate or other reasons. Consequently, trees develop many flower buds that blossom in the following year. The many flowers lead to a high number of small fruits that won’t be accepted on the market. Endogenous factors (phytohormones and carbohydrate allocation) subsequently establish the biennial cycle. The alternate bearing... A. Betz, H. Benny, M. Jens, M. Özyurtlu, M. Pflanz, T. Rachow-autrum, A. Schischmanow, M. Scheele, J. Schrenk, L. Schrenk, M. Zude, R. Gebbers

9. Value Of Connectivity In Rural Areas: Case Of Precision Agriculture Data

The introduction of precision agricultural technologies in the early 1990’s was made possible through the utilization of global positioning system (GPS). However, unlike GPS which has worldwide coverage allowing field-level precision agricultural activities to occur. Collecting spatial and machinery data into a repository efficiently is not currently feasible in real-time due to lack of broadband and wireless connectivity in many rural areas even in developed counties. Lack... T. Griffin, T. Mark

10. A Method To Estimate Irrigation Efficiency With Evapotranspiration Data

Irrigation efficiency is defined as the ratio of irrigation water consumed by the crops to the water diverted (Wg) from a river or reservoir or wells. This terminology serves for better irrigation systems designation and irrigation management practices improvement. But it is hard or high cost with labor intensity to estimate irrigation efficiency from field measurement. This paper proposes an estimating method of irrigation efficiency at the scale of irrigation... H. Zeng, B. Wu, N. Yan

11. Suitability Of Fluorescence Sensors To Estimate The Susceptibility Degree Of Spring Barley To Powdery Mildew And Leaf Rust

The overall role of precision agriculture is not restricted to those systems for in-field and in-season sensing of the impact of stresses. Much more, its contribution comprises the prevention of stresses, amongst others by supporting the selection of appropriate and stress-tolerant genotypes in breeding programs. In this context, the development, selection and use of cultivars which are tolerant to pathogens establish an essential tool for a more sustainable and environmental-friendly... G. Leufen, G. Noga, M. Hunsche

12. Selection Of Fluorescence Indices For The Proximal Sensing Of Single And Multiple Stresses In Sugar Beet

The use of fluorescence indices for sensing the impact of abiotic and biotic stresses in agricultural crops is well documented in the literature. Pigment fluorescence gives a precise picture about the plant physiology and its changes following the occurrence of stresses. In general, alterations in such optical signals is caused either by the stress-induced accumulation of one or more fluorophores, or the degradation of specific molecules like chlorophyll. Unfortunately, many stresses... G. Leufen, G. Noga, M. Hunsche

13. Study Of Spatio-Temporal Variation Of Soil Nutrients In Paddy Rice Planting Farm

It is significant to analysis the spatial and temporal variation of soil nutrients for precision agriculture especially in large-scale farms. For the data size of soil nutrients grows once after sampling which mostly by the frequency of one year or months, to discover the changing trends of exact nutrient would be instructive for the fertilization in the future. In this study, theories of GIS and geostatistics were used to characterize the spatial and temporal variability of soil... C. Wang, T. Chen, J. Dong, C. Li

14. Disease Scouting For Aerial Blight Based On Logical Areas Of Collection In Soybean Fields Rotated With Rice

Rhizoctonia solani AG1-IA causes sheath blight in rice and aerial blight in soybean.  In Arkansas, rice and soybean rotations facilitate a continuous source of R. solani AG1-IA inoculum from one year to the next.    Aerial blight is a two stage disease where colonization of the plant occurs during the early vegetative growth stages and aerial blight symptoms occur during the reproductive growth stages after canopy closure.  At canopy closure,... C.S. Rothrock, W.S. Monfort, T.W. Griffin, T.N. Spurlock

15. Site-specific Scale Efficiency Determined by Data Envelopment Analysis of Precision Agriculture Field Data

Since its inception and acceptance as a benchmarking tool within the economics literature, data envelopment analysis (DEA) has been used primarily as a means of calculating and ranking whole-farm entities marked as decision making units (DMU) against one another.  Within this study, instead of ranking the entire farm operation against similar peers that encompass the study, individual data points from within the field are evaluated to analyze the site-specific technical efficiencies estimated... J.L. Maurer, T.W. Griffin, A. Sharda

16. 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

17. Value of Map Sharing Between Multiple Vehicles Using Automated Section Control in the Same Field

Large area farms and even moderate sized farms employing custom applicators and harvesters have multiple machines in the same field at the same time conducting the same field operation.  As a method to control input costs and minimize application overlap, these machines have been equipped with automatic section control (ASC). Over application is a concern especially for more irregularly shaped fields; however modern technology including automated guidance combined with automatic section control... J. Bennett, C. Wilson, A. Sharda, T. Griffin

18. Real-time Gauge Wheel Load Variability on Planter with Downforce Control During Field Operation

Downforce control allows planters to maintain gauge wheel load across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and either set to manually or automatically control the gauge wheel load. This technology uses load cells to actively regulate downforce on individual row units by monitoring target load on the gauge wheels. However, no studies have been conducted to evaluate the variability in gauge wheel load observed during planter operation... A. Sharda, S. Badua, D. Flippo, I. Ciampitti, T.W. Griffin

19. Evaluation of Utilization Potential for Methods of Georeference in the Management of Weed Contamination of Potato Cultures

Combating crop contamination with harmful invasive species is one of the main themes of agricultural research. For the potato cultures, the weed contamination decreases not only the quality but also the quantity of the harvest. The most invasive contamination for this culture is represented by the Agropyron repens and Sorgum halepense, two invasive and very nocive species characterized by underground stems able to penetrate the potato¢s tubercle and decrease their storage... L. Musetescu, M. Gidea

20. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli

21. Agronōmics: Eliciting Food Security from Big Data, Big Ideas and Small Farms

Most farmers globally could make their farms more productive; few are limited by ambient availabilities of light energy and water. Similarly the sustainability of farming practices offers large scope for innovation and improvement. However, conventional ‘top-down’ Agricultural Knowledge and Innovation Systems (AKISs) are commonly failing to maintain significant progress in either productivity or sustainability because multifarious and complex agronomic interactions thwart accurate... R. Sylvester-bradley, D. Kindred, P. Berry

22. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of Corn

Uniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground speed... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin

23. The Impact of Precision Agriculture Technologies on Farm Profitability in Kansas

Even with more than a decade long adoption of the precision agriculture (PA) technologies in the United States, its impact on farm profitability is still not clear. This paper uses farm level data from Kansas Farm Management Association (KFMA) to conduct the ex-post evaluation of PA technologies on farm profitability in Kansas. The analysis of the data using propensity score matching method indicates that there is on an average $60,000 difference in net returns of the farm with at least one PA... S. Dhoubhadel, T.W. Griffin

24. Yield Mapping in Fruit Farming

Due to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz

25. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress Status

Previous research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca,... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva

26. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry

Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White

27. Enhancing Spatial Resolution of Maize Grain Yield Data

Grain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops.  Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used to... J. Siegfried, R. Khosla, D. Mandal, W. Yilma