Proceedings
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| Filter results7 paper(s) found. |
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1. Compatible ISOBUS Applications Using a Computational Tool for Support the Phases of the Precision Agriculture Cycle... W.C. Lopes, G. Domingues, R.V. Sousa, A.J. Porto, R.Y. Inamasu, R.R. Pereira |
2. Assessing the Variability of Red Stripe Disease in Louisiana Sugarcane Using Precision Agriculture MethodsSymptoms of red stripe disease caused by Acidovorax avenae subsp. avenae in Louisiana between 1985 and 2010 were limited to the leaf stripe form which caused no apparent yield loss. During 2010, the more severe top rot form was observed, and a study was initiated to investigate the distribution of red stripe in the field and determine its effects on cane and sugar yields. Two fields of cultivar HoCP 00-950, one plant-cane (PC) crop and one first-ratoon (FR) crop, affected by top rot were... R.M. Johnson, M.P. Grisham |
3. Economics of Gps-enabled Navigation TechnologiesTo address the economic feasibility of global positioning system (GPS) enabled navigation technologies including automated guidance and lightbar, a linear programming model was formulated using data from Midwestern U.S. Corn Belt farms. Five scenarios were compared: (i) a baseline scenario with foam, disk or other visual marker reference, (ii) lightbar navigation with basic GPS availability (+/-3 dm accuracy), (iii) lightbar with satellite subscription correction GPS (+/-1 dm), (iv) automated... T.W. Griffin, D.M. Lambert, J. Lowenberg-deboer |
4. Synchronized Windrow Intelligent Perception System (SWIPE)The practice of bale production, in forage agriculture, involves various machines that include tractors, tedders, rakers, and balers. As part of the baling process, silage material is placed in windrows, linearly raked mounds, to drive over with a baler for easy collection into bales. Traditionally, a baler is an implement that is attached on the back of a tractor to generate bales of a specific shape. Forage agricultural equipment manufacturers have recently released an operator driven, self-propelled... E.M. Dupont, P.R. Kolar |
5. Use of MLP Neural Networks for Sucrose Yield Prediction in SugarbeetINTRODUCTION Sugar beet is one of the more technified agro industries in Spain. In the last years, it has leaded as well the digital transformation with the objective of maintaining sugar beet competitivity both national and internationally. Among other lines, very high potential has been identified in determining the sucrose content using a combination of Artificial Intelligence and Remote Sensing. This work presents the conclusions of an extensive data acquisition task, creation of... M. Cabrera dengra, C. Ferraz pueyo, V. Pajuelo madrigal, L. Moreno heras, G. Inunciaga leston, R. Fortes |
6. Functional Soil Property Mapping with Electrical Conductivity, Spectral and Satellite Remote SensorsProximal electrical conductivity (EC) and spectral sensing has been widely used as a cost-effective tool for soil mapping at field scale. The traditional method of calibrating proximal sensors for functional soil property prediction (e.g., soil organic matter, sand, silt, and clay contents) requires the local soil sample data, which results in a field-specific calibration. In this large-scale study consisting of 126 fields, we found that the traditional local calibration method had suffered weak... X. Xiong, D. Myers, J. Debruin, B. Gunzenhauser, N. Sampath, D. Ye, H. Underwood, R. Hensley |
7. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management ZonesInvestment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe |