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Analyzing Sensor Data at the Source  Public Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  pp. 1-14
Authors:   Sierra Young
Keywords:   data science, instruction, sensors, calibration, measurements.

Abstract. Data science spans a broad array of activities, including data collection, storage, integration, analysis, inference, communication, and ethics. Exposure to these concepts in the context of real-world applications is critical so students can understand the limitations and other considerations that may arise when applying data science concepts. Of particular importance for biological and agricultural engineering data science applications is the acquisition and availability of sensor data. Sensor technologies are continuously being incorporated into modern agricultural and environmental practices and measure physical quantities such as temperature, light, pressure, sound, and humidity. When using sensors, physical phenomena are converted into detectable electrical signals, which must be ultimately processed into meaningful measurements. It is important that students have some understanding of how most sensor responses are first represented by a change in electrical property, and how that electrical property may be converted into the physical, measured phenomenon. Therefore, the purpose of this two-part instructional material is to familiarize students with the basic concepts of raw sensor output data by providing activities focused on linear sensor calibration and characterizing nonlinear temperature responses.

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