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.
Reza Rasinojehdehi; Goran Cirovic
Abstract
In recent years, there has been a surge in research exploring the potential of Machine Learning (ML) for predicting water pump failures. While some studies have focused on supervised approaches, others have delved into unsupervised methods. However, the challenge lies in identifying the key variables ...
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In recent years, there has been a surge in research exploring the potential of Machine Learning (ML) for predicting water pump failures. While some studies have focused on supervised approaches, others have delved into unsupervised methods. However, the challenge lies in identifying the key variables crucial for accurate failure predictions. This study bridges this gap by consulting domain experts to discern essential variables, including water catchment area level, water quality index, lubrication frequency, water reservoir temperature, operating time, and power interruptions count. Employing supervised ML methods, specifically multiple regression and decision tree cart, the research aims to enhance the precision of failure predictions, shedding light on less-explored variables that play a significant role in pump failure.
