Methodological aspects of determining nitrogen fixation of different forage legumes

Knowledge about the amount of fixed nitrogen of different legume crops is very important for calculation of farm N balances. According to literature the choice of determination method may have an impact on the estimated amount of N fixed by a legume sward. The aim of the study was to compare the three most important field methods for determination of nitrogen fixation under different sward management systems. In the present study the natural 15N abundance method gave lower fixation rates than the two alternative methods (total-N-difference method and 15N enrichment technique). The determination of N fixation based only on N in harvestable plant material underestimated the amount of fixed N on average by 70 kg ha-1 compared to techniques including also the amount of N in non harvestable plant part

Lane Boundary Detection Using Statistical Criteria

This paper describes an approach of lane boundary detection especially for country roads fields by artificial vision. It uses statistical criteria such as energy, homogeneity, contrast etc. to distinguish between road and non-road-area. To model the borders of the road and to accelerate the method, we combine it with a model based image segmentation technique. The model is adapted to the found lane boundaries by a direct estimation of the model parameters which is based on chi-square fitting. Additionally we will show that the grey value statistics for video images of ourdoor scenes are independent from the estimation direction for small distances. KEYWORDS Vision, robotics, mobile robots, lane boundary detection, second order statistical criteria, road models, deformable templates, chi-square fitting. 1. INTRODUCTION Lane boundary detection for mobile robots is mostly vision based. If vision is used, lane boundary detection is normally based on grey value (color) gradients [1, 2, 3..

Chi-Square Fitting Of Deformable Templates For Lane Boundary Detection

: An accelerated algorithm for lane boundary detection is described. It combines random search with the chi-square fitting to obtain the best set of parameters of a deformable template. The lane boundaries found in a grey level image are modelled via deformable templates. To find the best fitting template a likelihood function is used. It is based on the degree of match (in magnitude and direction) between the deformable template and the underlying lane boundaries. Chi--square fitting is applied on a randomly chosen template to maximise the likelihood function. The experimental results demonstrate the performance of this improved algorithm. Keywords: Robots, vision, lane boundary detection, deformable templates, maximum likelihood, chi-square fitting. 1. INTRODUCTION The detection of lane boundaries in images of road scenes, captured from a ground-level visual sensor, is an important ability for navigation and guidance of mobile autonomous robots. All applications of autonomous robot..