@article{(Open Science Index):https://publications.waset.org/pdf/10006660,
	  title     = {Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops: Statistical Evaluation of the Potential Herbicide Savings},
	  author    = {Morten Stigaard Laursen and  Rasmus Nyholm Jørgensen and  Henrik Skov Midtiby and  Anders Krogh Mortensen and  Sanmohan Baby},
	  country	= {},
	  institution	= {},
	  abstract     = {This work contributes a statistical model and simulation
framework yielding the best estimate possible for the potential
herbicide reduction when using the MoDiCoVi algorithm all the
while requiring a efficacy comparable to conventional spraying. In
June 2013 a maize field located in Denmark were seeded. The field
was divided into parcels which was assigned to one of two main
groups: 1) Control, consisting of subgroups of no spray and full dose
spraty; 2) MoDiCoVi algorithm subdivided into five different leaf
cover thresholds for spray activation. In addition approximately 25%
of the parcels were seeded with additional weeds perpendicular to
the maize rows. In total 299 parcels were randomly assigned with
the 28 different treatment combinations. In the statistical analysis,
bootstrapping was used for balancing the number of replicates. The
achieved potential herbicide savings was found to be 70% to 95%
depending on the initial weed coverage. However additional field
trials covering more seasons and locations are needed to verify
the generalisation of these results. There is a potential for further
herbicide savings as the time interval between the first and second
spraying session was not long enough for the weeds to turn yellow,
instead they only stagnated in growth.},
	    journal   = {International Journal of Agricultural and Biosystems Engineering},
	  volume    = {11},
	  number    = {4},
	  year      = {2017},
	  pages     = {272 - 281},
	  ee        = {https://publications.waset.org/pdf/10006660},
	  url   	= {https://publications.waset.org/vol/124},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 124, 2017},
	}