Seyyed Ali Nourkhah; Goran Cirovic; Seyyed Ahmad Edalatpanah
Abstract
Smart agriculture, also known as precision agriculture, allows farmers to maximize yields using minimal resources such as water, fertilizer, and seeds. By deploying sensors and mapping fields, farmers can begin to understand their crops at a micro-scale, conserve resources, and reduce impacts on the ...
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Smart agriculture, also known as precision agriculture, allows farmers to maximize yields using minimal resources such as water, fertilizer, and seeds. By deploying sensors and mapping fields, farmers can begin to understand their crops at a micro-scale, conserve resources, and reduce impacts on the environment. Smart agriculture has roots in the 1980s when Global Positioning System (GPS) capability became accessible for civilian use. Once farmers could map their crop fields accurately, they could only monitor and apply fertilizer and weed treatments to areas that required it. During the 1990s, early precision agriculture users adopted crop yield monitoring to generate fertilizer and pH correction recommendations. As more variables could be measured and entered into a crop model, more accurate recommendations for fertilizer application, watering, and even peak yield harvesting could be made. Throughout the long term, shrewd cultivating has become valuable to all ranchers-little and huge scope.
Hamiden Abd El- Wahed Khalifa; Seyyed Ahmad Edalatpanah; Darko Bozanic
Abstract
Smoothing (filtering) of data is a major problem in engineering and science. In this paper, the smoothing of data arising in modelling and decision-making problems is considered. Firstly, the conventional smoothing and filtering problem and its extension to a fuzzy situation are introduced.
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Smoothing (filtering) of data is a major problem in engineering and science. In this paper, the smoothing of data arising in modelling and decision-making problems is considered. Firstly, the conventional smoothing and filtering problem and its extension to a fuzzy situation are introduced.
