Proceedings
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| Filter results18 paper(s) found. |
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1. Use of Chemical and Physical Attributes Of the Soil in Management Units DefinitionSeveral equipments and methodologies have been developed to make available precision agriculture, especially the high cost of its implantation and sampling. An interesting possibility... C.L. Bazzi, E.G. Souza, L.H. Nobrega, M.A. Uribe-opazo, D.M. Rocha |
2. 3D Acquisition System Applied to Agronomic ScenesTo enable a better decision making by the farmer in order to optimize the crop management, it is essential to provide a set of information on basic parameters of the crops. These information are numerous and the image processing is increasingly used for disease detection, weed detection or yield estimation. We will focus initially on assessing the yield of a wheat crop in automatic way. This yield is directly related to the number of ears per square meter for which the counting is currently... F. Cointault, P. Gouton, B. Billiot |
3. Spatial Econometric Approaches to Develop Site-Specific Nematode Management Strategies in Cotton ProductionRoot-knot nematode infestations tend to be spatially clustered within agricultural... Z. Liu, T. Griffin, T. Kirkpatrick, S. Monfort |
4. Aggregating Precision Agriculture Data Across RegionsThe analysis of precision agricultural data has largely focused on one field at a time and to a lesser extent to one individual farm. Recent developments have allowed those with access to data from across large regions to realize additional value by pooled community analysis of precision agriculture data. Pool data analysis has provided greater value to individual farms than they would have gained by only using their own farm-level data. Statistical, economic, and risk methodologies... T. Griffin |
5. Making the Most of Precision Ag Data: Big Data in Farm Managementna ... T. Griffin |
6. The Cost Of Dependence Upon GPS-enabled Navigation TechnologiesThe adoption of global positioning system (GPS) technology to fine-tune agricultural field operations over the last decade has been unprecedented relative to other agricultural technologies. Resultantly, as agricultural machinery size and capacity increased, field operations have become much more precise due to the synergistic relationship between farm machinery and GPS-enabled guidance technology. With increased dependence upon GPS technology, one must ask “What are the risks associated... C. Lee, T. Griffin |
7. Adoption And Use Of Precision Agriculture Technologies By PractitionersA survey of farmers and farm service providers were initiated to ascertain the adoption and use of precision agriculture technologies as well as the barriers to and incentives for adoption. Farm-level data were collected via audience response system at the 2009 Alabama Precision Ag and Field Crops Conference and local winter production meetings across the six crop reporting districts in Alabama. Service provider data were collected using an online survey. Questions common to farmers and service... A.T. Winstead, S.H. Norwood, T. Griffin, A.M. Adrian, M. Runge, J.P. Fulton |
8. Worldwide Adoption Of Precision Agriculture Technology: The 2010 UpdatePrecision agriculture technology has been on the market for nearly two decades; and the question remains regarding how and to what extent farmers are making the best use of the technology. Yield monitors, GPS-enabled guidance technology, farm-level mapping and GIS software, on-the-go variable rate applications, and other spatial technologies are being used by thousands of farmers worldwide. The USDA Agricultural Resource Management Survey (ARMS) and the annual CropLife/Purdue University Precision... T. Griffin, J. Lowenberg-deboer |
9. Precision Sensors For Improved Nitrogen Recommendations In WheatCrop sensor-based systems with developed algorithms for making mid-season fertilizer nitrogen (N) recommendations are commercially available to producers in some parts of the world. Although there is growing interest in these technologies by grain producers in Montana, use is limited by the lack of local research under Montana’s semiarid conditions. A field study was carried out at two locations in 2011, three locations in 2012, and two locations in 2013 in North West Montana:... O.S. Walsh, A. Pandey, R. Christiaens |
10. Towards Automated Pneumatic Thinning Of Floral Buds On Pear TreesThinning of pome and stone fruit is an important horticultural practice that is used to enhance fruit set and quality by removing excess floral buds. As it is still mostly conducted through manual labor, thinning comprises a large part of a grower’s production costs. Various thinning machines developed in recent years have clearly demonstrated that mechanization of this technique is both feasible and cost effective. Generally, these machines still lack sufficient selectivity... N. Wouters, R. Van beers, B. De ketelaere, T. Deckers, J. De baerdemaeker, W. Saeys |
11. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
12. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural EducationThe industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby |
13. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
14. Ownership and Protections of Farm DataFarm data has been a contentious point of debate with respect to ownership rights and impacts when access rights are misappropriated. One of the leading questions farmers ask deals with the protections provided to farm data. Although no specific laws or precedence exists, the possibility of trade secret is examined and ramifications for damages discussed. Farm management examples are provided to emphasize the potential outcomes of each possible recourse for misappropriating farm data. ... A. Ellixson, P. Goeringer, T. Griffin |
15. Managing the Kansas Mesonet for Site Specific Weather InformationWeather data has become one of the most widely discussed layers in precision agriculture especially in terms of agricultural ‘big data’. However, most farmers (and even other researchers outside of meteorology) are not likely aware of the complexities required to maintain weather stations that provide data. These stations are exposed to the elements 24/7 and provide unique challenges for sustainment during extreme weather conditions. Based upon decades of experience, this paper discusses... T. Griffin, C. Redmond, M. Knapp |
16. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 CountriesReducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: occurrence... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele |
17. Operationalization of On-farm Experimentation in African Cereal Smallholder Farming SystemsPast efforts have concentrated on linear or top-down approaches in delivering precision nutrient management (PNM) practices to smallholder farmers. These deliberate attempts at increasing adoption of PNM practices have not yielded the expected outcomes, that is, increased productivity and nutrient use efficiency, at scale. This is because technologies generated by scientists with minimal farmer involvement often are not well tailored to the attendant agro-ecological, socio-economic, and cultural... I. Adolwa, S. Phillips, B.A. Akorede, A.A. Suleiman, T. Murrell, S. Zingore |
18. Harnessing Farmers’, Researchers’ and Other Stakeholders’ Knowledge and Experiences to Create Shared Value from On-farm Experimentation: Lessons from KenyaAchieving greater sustainability in farm productivity is a major challenge facing smallholder farmers in Kenya. Existing technologies have not solved the challenges around declining productivity because they are one-size-fits-all that doesn’t account for the diverse smallholder contexts. A study was carried out in Kenya by a multi-disciplinary team to assess the value of On-Farm Experimentation (OFE) to tailor technologies to local conditions. The OFE process begun with identification of... J. Muthamia, I. Adolwa, J. Mutegi, S. Zingore, S. Phillips |