Effects of Iron, Zinc and Manganese and Method of Their Application on Phonology, Yield and Grain Quality of Sweet Corn

In order to evaluate effects of iron, zinc and manganese, and the methods of applying them on yield and yield components of sweet corn, an experiment was performed as factorial based on randomized complete block design with four replications at the Research Farm of Urmia University, during 2009-2010. Experimental factors were micronutrient fertilizers with four levels (control, iron, zinc and manganese) and micronutrient applying method with two levels (foliar and soil application). Results indicated that Interaction between experimental factors on plant height, ear diameter, grain size, 1000-grain weight, biological yield and protein percentage was significant. In most of these traits foliar application of Fe and Zn showed better results than foliar application of Mn. Also, by influenced of micronutrients applying method, foliar application was caused 13.25%, 5.38% and 24.84% increasing in numbers of grain per ear, grain yield and soluble sugar percentage. In additional soluble sugar percentage was influenced significantly by application of micronutrient elements. Based on the results, foliar application of micronutrient elements showed better results than soil application of them

Road Drainage System Localisation and Condition Data Capture

Inspection of road networks is time and cost consuming. Over half of the money allocated for road maintenance are spent on this area. Visual inspection is still the most commonly employed way of inspection for most parts of the road network. The sheer amount of lane miles of the road network renders this type of road monitoring a costly process. The ultimate goal of this paper is to reduce the time and cost needed to perform routine drive-by, visual and conditional data capturing. The road inspector devotes effort to visually capture the great number of the road assets as it consists of capturing of both the geometry and condition of multiple road assets. In this paper, we propose a method to capture road drainage covers through images and assess if they could potentially be blocked or not. This proposed novel framework focuses on two main tasks: a) localisation of road drainage b) assessment of drainage condition (if they are clean or not). This solution uses Speeded up Robust Features (SURF) and Scale Invariant Feature Form (SIFT) detector, the Bag of Visual Words (BoVW) enhanced with k-means algorithms for grouping the features, and a Support Vector Machine classifier for classifying the data to their respective categories