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McLellan, E
Siliveru, K
Smith, J
Bussher, W.J
Hillyer, C.C
Hatfield, G
Bagavathiannan, M
Harms, H
Zhao, X
Nieto, J
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Authors
Bedard, F
Reichert, G
Dobbins, R
Pantel, M
Smith, J
Happich, G
Lang, T
Harms, H
Sela, S
van-Es, H
McLellan, E
Melkonian, J
Marjerison , R
Constas, K
Bauer, P.J
Stone, K.C
Bussher, W.J
Millen, J.A
Evans, D.E
Strickland, E.E
Walter, A
Khanna, R
Lottes, P
Stachniss, C
Siegwart, R
Nieto, J
Liebisch, F
Hatfield, G
Reicks, G
Carter, E
Lu, J
Chen, Z
Miao, Y
Li, Y
Zhang, Y
Zhao, X
Jia, M
Ferreyra, R
Hillyer, C.C
Fuller, H.D
Craker, B
Watanabe, K
Samborski, S.M
Torres, U
Leszczyńska, R
Bech, A
Bagavathiannan, M
Armstrong, P.R
Pordesimo, L.O
Siliveru, K
Gerken, A.R
Serfa Juan, R.O
Topics
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
Precision Nutrient Management
Applications of Unmanned Aerial Systems
On Farm Experimentation with Site-Specific Technologies
In-Season Nitrogen Management
Data Analytics for Production Ag
Scouting and Field Data collection with Unmanned Aerial Systems
Artificial Intelligence (AI) in Agriculture
Type
Oral
Poster
Year
2010
2016
2008
2018
2022
2024
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Filter results10 paper(s) found.

1. Timely, Objective, And Accurate Crop Area Estimations And Mapping Using Remote Sensing And Statistical Methods For The Province Of Prince Edward Island, Canada

The provincial government of Prince Edward Island, Canada, required timely, objective, and accurate annual crop area statistics and mapping for 2006 to 2008. Consequently, Statistics Canada conducted a survey incorporating medium- resolution satellite imagery (10 to 30 m) and statistical survey methods. The objective was to produce crop area estimates with a coefficient of variation (CV) as a measure of accuracy, and to produce maps showing the distribution and location of different crops and... F. Bedard, G. Reichert, R. Dobbins, M. Pantel, J. Smith

2. Spatial Modelling Of Agricultural Crops For Parallel Loading Operations

There is a trend in agricultural engineering towards high-performance harvesting machines with growing operating width and throughput. As much as performance and throughput are rising, the transportation units are characterized by increasing transportation volume. If harvesting and transport are combined in parallel operation (e.g. self-propelled forage harvester), the driver of the harvesting machine and the driver of the transport unit has to pay highest attention to the loading process.... G. Happich, T. Lang, H. Harms

3. Using the Adapt-N Model to Inform Policies Promoting the Sustainability of US Maize Production

Maize (Zea mays L.) production accounts for the largest share of crop land area in the U.S. It is the largest consumer of nitrogen (N) fertilizers but has low N Recovery Efficiency (NRE, the proportion of applied N taken up by the crop). This has resulted in well-documented environmental problems and social costs associated with high reactive N losses associated with maize production. There is a potential to reduce these costs through precision management, i.e., better application timing, use... S. Sela, H. Van-es, E. Mclellan, J. Melkonian, R. Marjerison , K. Constas

4. Site-specific Irrigation of Peanuts on a Coastal Plain Field

Irrigator-Pro is an expert system that prescribes irrigation for corn (Zea mays L.), cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea). We conducted an experiment in 2007 to evaluate Irrigator-Pro as a tool for variable rate irrigation of peanut using a site-specific center pivot irrigation system. Treatments were irrigation of whole plots based on the expert system, irrigation of individual soils within plots based on the expert system, irrigation of individual...

5. Flourish - A Robotic Approach for Automation in Crop Management

The Flourish project aims to bridge the gap between current and desired capabilities of agricultural robots by developing an adaptable robotic solution for precision farming. Combining the aerial survey capabilities of a small autonomous multi-copter Unmanned Aerial Vehicle (UAV) with a multi-purpose agricultural Unmanned Ground Vehicle (UGV), the system will be able to survey a field from the air, perform targeted intervention on the ground, and provide detailed information for decision support,... A. Walter, R. Khanna, P. Lottes, C. Stachniss, R. Siegwart, J. Nieto, F. Liebisch

6. Can Unreplicated Strip Trials Be Used in Precision On-Farm Experiments?

On-farm experiments are used to evaluate a wide variety of products ranging from pesticide and fertilizer rates to the installation of tile drainage. The experimental design for these experiments is usually replicated strip trials.  Replication of strip trials is used to estimate experimental error, which is the basis for judging statistical significance of treatment effects. Another consideration for using strip trials is greater within-field variability than smaller fields used... G. Hatfield, G. Reicks, E. Carter

7. 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

8. Interoperability As an Enabler for Principled Decision-making in Irrigation: the Precision Agriculture Irrigation Language (PAIL)

Fresh water is a scarce resource, and agriculture consumes a high fraction of it worldwide. As climate change increases the likelihood of high temperatures and droughts, irrigation becomes an increasingly attractive option for managing crop production risks. Unfortunately, and despite decades of efforts by professional associations to promote the use of a principled, data-driven approach to irrigation scheduling often called scientific irrigation scheduling (SIS), the fraction of farmers... R. Ferreyra, C.C. Hillyer, H.D. Fuller, B. Craker, K. Watanabe

9. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding Trials

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at three... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan

10. Advanced Classification of Beetle Doppelgängers Using Siamese Neural Networks and Imaging Techniques

The precise identification of beetle species, especially those that have similar macrostructure and physical characteristics, is a challenging task in the field of entomology. The term "Beetle Doppelgängers" refers to species that exhibit almost indistinguishable macrostructural characteristics, which can complicate tasks in ecological studies, conservation efforts, and pest management. The core issue resides in their striking similarity, frequently confusing both experts and automated... P.R. Armstrong, L.O. Pordesimo, K. Siliveru, A.R. Gerken, R.O. Serfa juan