AS  Vol.9 No.11 , November 2018
A Miniature Sensor for Measuring Reflectance, Relative Humidity, and Temperature: A Greenhouse Example
Abstract: There is a growing interest in using miniature multi-sensor technology to monitor plant, soil, and environmental conditions in greenhouses and in field settings. The objectives of this study were to build a small multi-channel sensing system with ability to measure visible and near infrared light reflectance, relative humidity, and temperature, to test the light reflectance sensors for measuring spectral characteristics of plant leaves and soilless media, and to compare results of the relative humidity and temperature sensors to identical measurement obtained from a greenhouse sensor. The sensing system was built with off-the-shelf miniature multispectral spectrometers and relative humidity and temperature sensors. The spectrometers were sensitive to visible, red-edge, and near infrared light. The system was placed in a greenhouse setting and used to obtain relative reflectance measurements of plant leaves and soilless media and to record temperature and relative humidity conditions in the greenhouse. The spectrometer data obtained from plant leaf and soilless media were compatible with baseline spectral data collected with a hyperspectral spectroradiometer. The greenhouse was equipped with a relative humidity and temperature sensor. The relative humidity and temperature sensor measurements from our sensor system were strongly correlated with the relative humidity and temperature results obtained with the greenhouse sensors...
Cite this paper: Fletcher, R. and Fisher, D. (2018) A Miniature Sensor for Measuring Reflectance, Relative Humidity, and Temperature: A Greenhouse Example. Agricultural Sciences, 9, 1516-1527. doi: 10.4236/as.2018.911106.

[1]   Brand, O. (2006) Microsensor Integration into Systems-on-Chip. Proceedings of the IEEE, 94, 1160-1176.

[2]   Futagawa, M., Iwasaki, T., Murata, H., Ishida, M. and Sawada, K. (2012) A Miniature Integrated Multimodal Sensor for Measuring pH, EC and Temperature for Precision Agriculture. Sensors, 12, 8338-8354.

[3]   Fisher, D.K. and Gould, P.J. (2012) Open-Source Hardware Is a Low-Cost Alternative for Scientific Instrumentation and Research. Modern Instrumentation, 1, 8-20.

[4]   AMS-TAOS USA Inc. (2016) Spectral ID a New Class of Spectral Sensing Products. AS7262-Integrated 6-Channel Visible Spectrometer Covering 400-700 nm Wavelengths.

[5]   AMS-TAOS USA Inc. (2016) Spectral ID a New Class of Spectral Sensing Products. AS7263-Integrated 6-channel Nir Spectrometer Covering 600-900 nm Wavelengths.

[6]   Horler, D.N.H., Dockray, M. and Barber, J. (1983) The Red Edge of Plant Leaf Reflectance. International Journal Remote Sensing, 4, 273-288.

[7]   Jensen, J.R. (2000) Remote Sensing of the Environment: An Erath Resource Perspective. 2nd Edition. Prentice-Hall, New Jersey.

[8]   R Core Team (2018) R: A Language and Environment for statistical Computing. R Foundation for Statistical Computing, Vienna.

[9]   Asuero, A.G., Sayago, A. and Gonzalez, A.G. (2006) The Correlation Coefficient: An Overview. Critical Reviews in Analytical Chemistry, 36, 41-59.

[10]   Zady, M.F. (2009) Correlation and Simple Least Squares Regression.

[11]   Thenkabail, P.S., Gumma, M.K., Teluguntla, P. and Mohammed, I.A. (2014) Hyperspectral Remote Sensing of Vegetation and Agricultural Crops. Photogrammetric Engineering and Remote Sensing, 80, 697-709.

[12]   Prabhu, S.R., Gajendran, E. and Balakumar, N. (2016) Monitoring Atmospheric Conditions using Distributed Sensors. International Journal of Inventions in Engineering and Science Technology, 26, 108-120.