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Maja, J.M
Sanderson, J
Santana Neto, A.J
Majdi, M
Sørensen, C.G
Jens, M
Siegfried, J
Joseph, K
Shirzadi, A
Shannon, K
Ma, Y
Joseph, K
Akin, S
Myers, D.B
Mirzakhaninafchi, H
Mueller, D
Ahmad, A
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Authors
Majdi, M
Benjamin, D
Marie-France, D
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
Kremer, R.J
Kitchen, N.R
Sudduth, K.A
Myers, D.B
Myers, D.B
Kitchen, N.R
Sudduth, K.A
Leonard, B.J
Siegfried, J
Khosla, R
Longchamps, L
Song, X
Yang, G
Ma, Y
Wang, R
Yang, C
Souza, W.J
Akune, V.S
Benez, S.H
Citon, L.C
Nakazawa, P.H
Santana Neto, A.J
Khalilian, A
Qiao, X
Payero, J.O
Maja, J.M
Privette, C.V
Han, Y.J
Shirzadi, A
Maharlooei, M
hassanijalilian, O
Bajwa, S
Howatt, K
Sivarajan, S
Nowatzki, J
Souza, W.J
Benez, S.H
Nakazawa, P.H
Santana Neto, A.J
Citon, L.C
Akune, V.S
Scharf, P
Shannon, K
Sudduth, K
Kitchen, N
Shannon, K
Maharlooei, M
Bajwa, S
Mireei, S.A
Shirzadi, A
Sivarajan, S
Berti, M
Nowatzki, J
Morris, T
Tremblay, N
Kyveryga, P.M
Clay, D.E
Murrell, S
Ciampitti, I
Thompson, L
Mueller, D
Seger, J
Kumar, S
Singh, M
Mirzakhaninafchi, H
Modi, R.U
Ali, M
Bhardwaj, M
Soni, R
Wiseman, L
Sanderson, J
Ahmad, A
Aggarwal, V
Saraswat, D
El Gamal, A
Johal, G
Siegfried, J
Khosla, R
Mandal, D
Yilma, W
Jørgensen, R.N
Skovsen, S
Green, O
Sørensen, C.G
Akin, S
Arnall, B
Derrick, J
Akin, S
Sharry, R
Arnall, B
Muvva, V
Mwunguzi, H
Pitla, S
Joseph, K
Topics
Food Security and Precision Agriculture
Precision Horticulture
Proximal Sensing in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Decision Support Systems in Precision Agriculture
Sensor Application in Managing In-season Crop Variability
Agricultural Education
Proximal Sensing in Precision Agriculture
Standards & Data Stewardship
Small Holders and Precision Agriculture
Precision Agriculture and Global Food Security
Applications of Unmanned Aerial Systems
Robotics and Automation with Row and Horticultural Crops
Precision Agriculture and Global Food Security
On Farm Experimentation with Site-Specific Technologies
Type
Poster
Oral
Year
2012
2014
2016
2018
2022
2024
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Authors

Filter results22 paper(s) found.

1. 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

2. 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

3. 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

4. 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

5. Spectral Vegetation Indices to Quantify In-field Soil Moisture Variability

Agriculture is the largest consumer of water globally. As pressure on available water resources increases, the need to exploit technology in order to produce more food with less water becomes crucial. The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate. Spectral vegetation indices, especially Normalized Difference... J. Siegfried, R. Khosla, L. Longchamps

6. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth Season

This study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statistics... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang

7. Agronomic Characteristics of Green Corn and Correlations with Productivity for the Establishment of Management Zones in Vale Do Ribeira, SP, Brazil

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphic... W.J. Souza, V.S. Akune, S.H. Benez, L.C. Citon, P.H. Nakazawa, A.J. Santana neto

8. Utilizing Space-based Technology for Cotton Irrigation Scheduling

Accurate soil moisture content measurements are vital to precision irrigation management. Electromagnetic sensors such as capacitance and time domain reflectometry have been widely used for measuring soil moisture content for decades. However, to estimate average soil moisture content over a large area, a number of ground-based in-situ sensors would need to be installed, which would be expensive and labor intensive. Remote sensing using the microwave spectrum (such as GPS signals) has been used... A. Khalilian, X. Qiao, J.O. Payero, J.M. Maja, C.V. Privette, Y.J. Han

9. Greenhouse Study to Identify Glyphosate-resistant Weeds Based on Canopy Temperature

Development of herbicide-resistant crops has resulted in significant positive changes to agronomic practices, while repeated and intensive use of herbicides with the same mechanisms of action has caused the development of herbicide-resistant weeds. As of 2015, 35 weed species are reported to be resistant to glyphosate worldwide. A greenhouse study was conducted to identify characteristics which can be helpful in field mapping of glyphosate resistant weeds by using UAV imagery. The experiment included... A. Shirzadi, M. Maharlooei, O. Hassanijalilian, S. Bajwa, K. Howatt, S. Sivarajan, J. Nowatzki

10. Spatial Variability and Correlations Between Soil Attributes and Productivity of Green Corn Crop

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphic... W.J. Souza, S.H. Benez, P.H. Nakazawa, A.J. Santana neto, L.C. Citon, V.S. Akune

11. 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

12. Precision Farming Basics Manual - a Comprehensive Updated Textbook for Teaching and Extension Efforts

Today precision agricultural technologies are limited by the lack of a workforce that is technology literate, creative, innovative, fully trained in their discipline, able to utilize and interpret information gained from information-age technologies to make smart management decisions, and have the capacity to convert locally collected information into practical solutions. As part of a grant entitled Precision Farming Workforce Development:  Standards, Working Groups, and Experimental Learning... K. Shannon

13. Vis/NIR Spectroscopy to Estimate Crude Protein (CP) in Alfalfa Crop: Feasibility Study

The fast and reliable quality determination of alfalfa crop is of interest for producers to make management decisions, the dealers to determine the price, and the dairy producers for livestock management. In this study, the crude protein (CP), one of the main quality indices of alfalfa, was estimated using the visible and near-infrared (Vis/NIR) spectroscopy. A total of 68 samples from various variety trials of alfalfa crop were collected under the irrigated and rainfed conditions. The diffuse... M. Maharlooei, S. Bajwa, S.A. Mireei, A. Shirzadi, S. Sivarajan, M. Berti, J. Nowatzki

14. Rationale for and Benefits of a Community for On-Farm Data Sharing

Most data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger

15. Practical and Affordable Technologies for Precision Agriculture in Small Fields: Present Status and Scope in India

The objective of this review paper is to find out practical and affordable precision agriculture(PA) technologies present status and scope in India that are suitable for small fields. The judicious use of inputs like water, fertilizers, herbicides, pesticides and better management of farm equipments will increase the net profit for farmers. The important components of PA in India which are being used for small lands are Geographic Information System(GIS), laser land leveler, leaf color chart,... S. Kumar, M. Singh, H. Mirzakhaninafchi, R.U. Modi, M. Ali, M. Bhardwaj, R. Soni

16. 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

17. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal

18. 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

19. Enhancing Precision Agriculture Through Dual Weed Mapping: Delineating Inter and Intra-row Weed Populations for Optimized Crop Protection

In the field of precision agriculture, effective management of weed populations is essential for optimizing crop yield and health. This paper presents an innovative approach to weed management by employing dual weed mapping techniques that differentiate between inter-row and intra-row weed populations. Utilizing advanced imaging and data analysis of CropEye images collected by the Robotti robot from AgroIntelli (AgroIntelli A/S, Aarhus, Denmark), we have developed methods to generate distinct... R.N. Jørgensen, S. Skovsen, O. Green, C.G. Sørensen

20. The Evaluation of Spatial Response to Potassium in Soybeans

In agriculture, the nutrients that are in the largest demand are nitrogen (N), phosphorus (P), and potassium (K), as product demand increases  so does demand for fertilizers. In the case of potassium, most soils can provide potassium in amounts that exceed crop demand; however the potassium within the soil is not always readily available to the crop, this leads to producers apply potassium to their crops even though soil tests suggests otherwise. One such crop where potassium is in demand... S. Akin, B. Arnall

21. Influence of Potassium Variability on Soybean Yield

Due to its role as a plant essential nutrient, Potassium (K) serves as a fundamental component for plant growth. Soybeans are heavily reliant upon this nutrient for root growth and the production of pods, so much so that after nitrogen, potassium is the second most in-demand nutrient. Much of the overall soybean crop grown in Oklahoma is not managed with the fertility of K directly in mind. However, as the potential and expectation for greater yield increases, so does interest from producers... J. Derrick, S. Akin, R. Sharry, B. Arnall

22. Implementation of Autonomous Material Re-filling Using Customized UAV for Autonomous Planting Operations

This project introduces a groundbreaking use case for customized Unmanned Aerial Vehicles (UAVs) in precision agriculture, focused on achieving holistic autonomy in agricultural operations through multi-robot collaboration.  Currently, commercially available drones for agriculture are restrictive in achieving collaborative autonomy with the growing number of unmanned ground robots, limiting their use to narrow and specific tasks.  The advanced payload capacities of multi-rotor UAVs,... V. Muvva, H. Mwunguzi, S. Pitla, K. Joseph