Senior Design Projects

ECS193 A/B Winter & Spring 2021

Phenotyping platform: Plant and gall growth estimation, by using 2D images

Name Isabel Hernandez
Affiliation Postdoctoral Scholar

Project's details

Project title Phenotyping platform: Plant and gall growth estimation, by using 2D images
Background California produces 100 % of all walnuts grown in the United States, which has also been considered to be the 7th most valuable agricultural commodity during 2017 (California Department of Food and Agriculture, 2018). The walnut production can be negatively impacted by several biotic and abiotic factors such as irrigation deficit (Knipfer et al., 2020) and diseases i.e. crown gall, caused by Agrobacterium tumefaciens. For years, a group of plant breeders and pathologists have worked together to develop resistant walnut rootstocks, that will maintain high walnut productivity even under conditions of water deficit and plant disease. Our project aims to evaluate the interaction of water deficit and disease on plant growth. To accomplish this, we are developing a high through-put phenotyping platform that will allow us to characterize elite drought tolerant rootstocks to improve walnut production in California.
Description The project consisted of evaluating the two rootstock genotypes (RX1 and VX211), two water treatments (well-watered and water deficit) and two levels of the gall causing pathogen Agrobacterium tumefaciens (i.e. with and without infection). 2D-Images of the plant canopy were collected every week for a total of 5 weeks (Figure 1) for each plant in the trial. Moreover, the plants with infection, observed as a presence of a gall, were also photographed at the same time.

Figure 1. Canopy and gall images taken weekly during summer 2019 and 2020.

Deliverable Image specifications: 2988 x 3984 pixel, resolution 72 dpi. Each image was taken at a set distance between the white board and the camara and each image includes a reference with known dimensions.
Skill set desirable Past experiments demonstrated that the leaf area calculated from 2D images of the canopy, had a high correlation with leaf biomass (R2= 0.92, p < 0.0001), which is a huge advantage since it allows a non-destructive measurement. However, the leave area measurement is a time consuming task. What we would like to obtain from this project is:
- To optimize the 2D image analysis by transforming a large pool of 2D images into total area data for both the tree canopy and the galls (Figure 1).
- To discover a way to compare color changes in the canopy (i.e. yellowish leaves) over the course of the experiment for each of the individual plants photographed over the 5 week period.
Phone number 5304008562
Client time availability 30-60 min weekly or more
IP requirement Client wishes to keep IP of the project
Attachment Click here
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