2026-09-18T19:13:17Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/20757 2023-07-10 col_2142_5131 col_2142_14847 com_2142_5130 com_2142_683 com_2142_397

Reid, John F. Tarbell, Kenneth Alvin 2011-05-07T12:48:24Z 2011-05-07T12:48:24Z 10000-01-01 1990 Efficient utilization of agricultural resources requires a better understanding of crop growth and development. Current modeling efforts aimed at predicting the response of plants to environmental conditions lack the ability to relate results to basic characteristics observed in the field. The ability to reliably evaluate both photometric and morphometric parameters for individual plants would not only improve existing models, but also create a database from which new models may be generated. A vision-based data collection system was developed to study the growth and development of corn plants. Slide photographs were taken of field specimens at given intervals throughout the 1989 growing season. These images were scanned into the system and processed using software developed for this project. From 64 to 320 attributes were obtained for each plant and later combined with associated meteorological information to form a developmental database. A relationship between leaf area and length was derived and yielded a correlation coefficient (r$\sp2$) of 0.98. Also, a high correlation between measured and actual leaf length allowed the use of lengths measured from plant from views in leaf area estimations with an r$\sp2$ of 0.95. Given the average color for a leaf, it could be classified as either senesced or living using the green or red chromaticity values. Both classifiers had prediction confidences of about 95%. Using a prototype model building software package (AIMS) which combined inductive learning and optimization techniques, mathematical models were generated for plant leaf area, individual leaf areas, leaf physiology, leaf node heights, and overall plant dimensions as a function of time and temperature. All models performed well, with r$\sp2$ values ranging from 0.63 to 0.98 for leaf area models and 0.90 to 0.99 for all others. These models were combined to form a single growth and development model describing the canopy dynamics of the sampled crop. Made available in DSpace on 2011-05-07T12:48:24Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9114433.pdf: 4857069 bytes, checksum: ad3a590ac8f79b8880d4b414e62ce7d3 (MD5) Previous issue date: 1990 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:46:04Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:20:33-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9114433 (UMI)AAI9114433 http://hdl.handle.net/2142/20757 eng Copyright 1990 Tarbell, Kenneth Alvin Agriculture, General Engineering, Agricultural Artificial Intelligence Biology, Plant Physiology Characterizing corn growth and development using computer vision text Agricultural and Biological Engineering Agricultural Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.