Home> Published Issues> 2017> Volume 4, No. 2, June 2017
Autonomous Winter Wheat Variety Selection System
Felipe A. Guth 1,
Shane Ward 2, and
Kevin P. McDonnell 2
1. UCD School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland
2. UCD School of Biosystems and Food Engineering and UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
2. UCD School of Biosystems and Food Engineering and UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
Abstract—Public and private organizations have been investing significant financial and human resources to develop crop varieties suitable for different commercial destinations, regional characteristics and agronomic factors. The high number of variables and consequent complex analysis are factors that make the task of selecting a specific crop variety, that best fulfill the particularities of a given farm, a challenging one. In this scenario, this work proposes a ranking/decision method to deal with the stochastic problem of select a winter wheat variety, taking into account the random factors that influence in the specific decision. The system evaluates the commercial destination, site-specific and agronomic importance of varieties treats, such as resistance to diseases and lodging, to output a list of best winter wheat varieties choices, for a particular situation. The system's accuracy has been verified by experts of crop science, where a number of random outcomes were tested against specialist opinion.
Index Terms—variety selection, winter wheat, autonomous system, agricultural system, decision support system, seed selection
Cite: Felipe A. Guth, Shane Ward, and Kevin P. McDonnell, "Autonomous Winter Wheat Variety Selection System," Journal of Advanced Agricultural Technologies, Vol. 4, No. 2, pp. 104-110, June 2017. Doi: 10.18178/joaat.4.2.104-110
Cite: Felipe A. Guth, Shane Ward, and Kevin P. McDonnell, "Autonomous Winter Wheat Variety Selection System," Journal of Advanced Agricultural Technologies, Vol. 4, No. 2, pp. 104-110, June 2017. Doi: 10.18178/joaat.4.2.104-110