With growing concerns about global food supply and environmental impacts of modern agriculture, we are seeing an increased demand for more horticultural research. While research into plant genetics has seen an increased throughput from recent technological advancements, plant phenotypic research throughput has lagged behind. Improvements in open-source image processing software and image capture hardware have created an opportunity for the development of more competitively-priced, faster data-acquisition tools. These tools could be used to collect measurements of plants' phenotype on a much larger scale without sacrificing data quality. This paper demonstrates the feasibility of creating such a tool.
The resulting design utilized stereo vision and image processes in the OpenCV project to measure a representative collection of observable plant traits like leaflet length or plant height. After the stereo camera was assembled and calibrated, visual and stereo images of potato plant canopies and tubers(potatoes) were collected. By processing the visual data, the meaningful regions of the image (the canopy, the leaflets, and the tubers) were identified. The same regions in the stereo images were used to determine plant physical geometry, from which the desired plant measurements were extracted.
Using this approach, the tool had an average accuracy of 0.15 inches with respect to distance measurements. Additionally, the tool detected vegetation, tubers, and leaves with average Dice indices of 0.98, 0.84, and 0.75 respectively. To compare the tool's utility to that of traditional implements, a study was conducted on a population of 27 potato plants belonging to 9 separate genotypes. Both newly developed and traditional measurement techniques were used to collect measurements of a variety of the plants' characteristics. A multiple linear regression of the plant characteristics on the plants' genetic data showed that the measurements collected by hand were generally better correlated with genetic characteristics than those collected using the developed tool; the average adjusted coefficient of determination for hand-measurements was 0.77, while that of the tool-measurements was 0.66. Though the aggregation of this platform's results is unsatisfactory, this work has demonstrated that such an alternative to traditional data-collection tools is certainly attainable. / Master of Science / With growing concerns about global food supply and environmental impacts of modern agriculture, we are seeing an increased demand for more horticultural research. While research into plant genetics has seen an increased throughput from recent technological advancements, the throughput of research into how those genetic traits are expressed (plant phenotype) has lagged behind. Improvements in open-source image processing software and image capture hardware have created an opportunity for the development of more competitively-priced, faster data-acquisition tools. These tools could be used to collect measurements of plants’ phenotype on a much larger scale without sacrificing data quality. This paper demonstrates the feasibility of creating such a tool.
The tool developed in this work was an array of two USB-webcams that was capable of producing distance measurements. This was largely made possible by using software written in the C++ programming language maintained by the OpenCV project. The tool’s effectiveness was evaluated by comparing its measurement-taking ability to that of horticultural researchers measuring by hand. This comparison was made by using both measurement collection methods in the study of a population of potato plants. The result of this comparison was evidence that although the tool developed in this work was overall less effective at generating relevant measurements, more work on the project could yield improvements. Additionally, the tool developed improved the time spent per plant during measurement from 120 seconds to 14 seconds on average.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/75023 |
| Date | 13 February 2017 |
| Creators | Thoreson, Marcus Alexander |
| Contributors | Mechanical Engineering, Wicks, Alfred L., Southward, Steve C., Veilleux, Richard E., Nowak, Jerzy, Bird, John P. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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