Field Phenomics: A Web Based Image Analysis Platform Using Open Source Tools
- 0 Collaborators
My research has focused on development and implementation of software and hardware solutions to allow farmers, researchers, plant breeders, and physiologists to extract crop information. Using machine learning on data collected in forms of images, or using an IoT solution, we are able to make predictions about the health of the plants, and to correlate the results with yield, biomass, etc. These characteristics are very important when selecting or discarding a varietal. ...learn more
Project status: Under Development
Groups
Student Developers for AI
Intel Technologies
Intel Opt ML/DL Framework
Overview / Usage
This project is focused on the development of a web-based image analysis platform for phenomics using open source technologies. Using this, the scientific community, researchers, and farmers can extract physiological information through an image-based crop monitoring process. This freely-available program will perform high-throughput calculations of vegetation indices.
Methodology / Approach
The main goal of any field phenomics platform is to enable plant breeders to detect and evaluate dynamic traits which so far are difficult or inefficient to measure while also requiring destructive sampling. Plant phenotyping is frequently slower and more expensive than genotyping due to the time interval data capture for traits under different environments. Advanced remote sensing technologies such as aerial phenotyping may help expedite the development and selection of climate smart plant varieties. This is an ongoing research project. The main objective of this research is to build an open source web based image analysis platform using opencv (open source computer vision) to analyze field data in the form of images. Computation of vegetative indices to predict plant performance under real field conditions would be a provided outcome.
Technologies Used
OpenCV, Tensorflow, Node.js