Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Sriram Rajamani, Graduate Student
Lead Presenter's Name
Sriram Rajamani
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Monica Garcia
Abstract
This capstone project will encompass the use of uncrewed aerial vehicles to detect pathogens and diseases at an early stage and taking corrective measures avoiding losses. Precision agriculture integrates technology to promote sustainable farming, with crop monitoring providing agricultural researchers and engineers reliable insights into crop health and damage. The purpose of this research is to show efficient crop health monitoring with the use of autonomous technology, in this case an uncrewed aircraft with an RGB (red-green-blue) camera as the payload. To tell whether plants are infected with such diseases, physical discrepancies like holes can be shown easily from the human eye. However, an RGB camera can look deep into the plant to gather data that the human eye cannot perceive in terms of accuracy. At the current point of time, there aren’t results to show as the experiment is yet to occur. However, the data provided will be aerial RGB images and video footage of the plants that will be recorded by an uncrewed aircraft a few feet above the ground with a few angled shots processed through a few different trials. The data collected will be fed into an image analysis pipeline like PlantCV to be exposed to different image processing techniques. This will be done on the jyupter platform with python programming. What factors can determine and distinguish plant health? Different vegetation indices such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Index Area), and TGI (Triangular Greeness Index) help stakeholders understand the levels of health where their crops are at. NDVI Additionally, color changes and physical variations within plants are useful for the identification of any abnormal changes in plants. An example of this would be holes within leaves or a sign of fungus within plants. In short, these techniques are beneficial for automated real-time crop monitoring to reduce tedious manual inspections and mitigate the risks for field losses.
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?
No
Uncrewed Aerial Applications of Precision Agriculture
This capstone project will encompass the use of uncrewed aerial vehicles to detect pathogens and diseases at an early stage and taking corrective measures avoiding losses. Precision agriculture integrates technology to promote sustainable farming, with crop monitoring providing agricultural researchers and engineers reliable insights into crop health and damage. The purpose of this research is to show efficient crop health monitoring with the use of autonomous technology, in this case an uncrewed aircraft with an RGB (red-green-blue) camera as the payload. To tell whether plants are infected with such diseases, physical discrepancies like holes can be shown easily from the human eye. However, an RGB camera can look deep into the plant to gather data that the human eye cannot perceive in terms of accuracy. At the current point of time, there aren’t results to show as the experiment is yet to occur. However, the data provided will be aerial RGB images and video footage of the plants that will be recorded by an uncrewed aircraft a few feet above the ground with a few angled shots processed through a few different trials. The data collected will be fed into an image analysis pipeline like PlantCV to be exposed to different image processing techniques. This will be done on the jyupter platform with python programming. What factors can determine and distinguish plant health? Different vegetation indices such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Index Area), and TGI (Triangular Greeness Index) help stakeholders understand the levels of health where their crops are at. NDVI Additionally, color changes and physical variations within plants are useful for the identification of any abnormal changes in plants. An example of this would be holes within leaves or a sign of fungus within plants. In short, these techniques are beneficial for automated real-time crop monitoring to reduce tedious manual inspections and mitigate the risks for field losses.