Open Access. Powered by Scholars. Published by Universities.®
- Keyword
Articles 1 - 2 of 2
Full-Text Articles in Biological Engineering
Dynamic Classification Of Moisture Stress Using Canopy And Leaf Temperature Responses To A Step Changes Of Incident Radiation, Erin E. Stevens, George E. Meyer, Ellen T. Paparozzi
Dynamic Classification Of Moisture Stress Using Canopy And Leaf Temperature Responses To A Step Changes Of Incident Radiation, Erin E. Stevens, George E. Meyer, Ellen T. Paparozzi
Honors Theses
Environmental conditions affect plant productivity and understanding how plants respond to drought stress can be measured in different ways. This study focused on measuring leaf response time to induced water stress. Leaf response time to a step increase and step decrease in radiation was computed for four species of well-watered and water-stressed plants in a controlled environment. The canopy temperature was measured with an infrared thermometer and a thermal imaging camera. Thermal images were analyzed to determine the average temperature of a selected single, unobstructed leaf at the top of the canopy. Both the canopy response time and the single …
Using A Vnir Spectral Library To Model Soil Carbon And Total Nitrogen Content, Nuwan K. Wijewardane
Using A Vnir Spectral Library To Model Soil Carbon And Total Nitrogen Content, Nuwan K. Wijewardane
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression …