Developing a Pipeline to Automate Plant Growth Measuring
Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Collin Topolski, Graduate Student Paulina Slick, Senior Emma Costa, Freshman
Lead Presenter's Name
Collin Topolski
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Hugo Castillo
Abstract
Experiments following the growth of plants can suffer from inaccuracies stemming from the data collection process. Tracking this growth process manually requires a measurement tool and a systematic method to record the data throughout. However, the measured values can easily vary between observers which can cause deviations in the results. Additionally, the collection process can take up to an hour, even for small-scale experiments. To overcome this issue, images can be taken of the plants which can later be analyzed for measurements. By including an appropriate scale within the image, the pixels can be converted into distance units with more consistency and at a significant time reduction.
This work aims to create a pipeline that takes experimental plant growth images, analyzes them for physical measurement characteristics, and then provides easily understandable results. More specifically, some characteristics to be assessed are plant height, leaf area, and leaf count which can then be used to estimate further variables such as photosynthetic rates and wet mass. Through this automation, future plant growth studies can be completed with more robust results and less time commitment from researchers. Initially, this work will be utilized for plant growth experiments using lunar regolith amended with manure.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Ignite Grant
Developing a Pipeline to Automate Plant Growth Measuring
Experiments following the growth of plants can suffer from inaccuracies stemming from the data collection process. Tracking this growth process manually requires a measurement tool and a systematic method to record the data throughout. However, the measured values can easily vary between observers which can cause deviations in the results. Additionally, the collection process can take up to an hour, even for small-scale experiments. To overcome this issue, images can be taken of the plants which can later be analyzed for measurements. By including an appropriate scale within the image, the pixels can be converted into distance units with more consistency and at a significant time reduction.
This work aims to create a pipeline that takes experimental plant growth images, analyzes them for physical measurement characteristics, and then provides easily understandable results. More specifically, some characteristics to be assessed are plant height, leaf area, and leaf count which can then be used to estimate further variables such as photosynthetic rates and wet mass. Through this automation, future plant growth studies can be completed with more robust results and less time commitment from researchers. Initially, this work will be utilized for plant growth experiments using lunar regolith amended with manure.