Image Processing for High-Throughput Phenotyping of Seeds

11 pages•Published: June 9, 2021

Venkat Margapuri, Chaney Courtney and Mitchell Neilsen

Abstract

High-throughput phenotyping of seeds is the assessment of seed morphometry to aid in the prediction of yield, tolerance, resistance, and development of seeds in various environmental conditions. The paper focuses on the application of 3D graphics to image processing as a means to conduct seed phenotyping better. The paper proposes two algorithms - similar in the outcome, but different in implementation. The algorithms perform image processing on a variety of seeds such as wheat, soy, sorghum, rough rice, white rice, and canola to arrive at their morphometric estimations. In the area of static image processing, addressed are at least three common yet significant problems of seed clusters on images, skewed images, and poor image quality. As a means to address the problems, we propose the use of low-cost physical components. The algorithms provide the estimated count, area, perimeter, length, and width of seeds within an image.

Keyphrases: 3d graphics, high throughput phenotyping, image processing, seed morphometry, watershed algorithm

In: Yan Shi, Gongzhu Hu, Takaaki Goto and Quan Yuan (editors). CAINE 2020. The 33rd International Conference on Computer Applications in Industry and Engineering, vol 75, pages 69-79.

Links:	https://easychair.org/publications/paper/RQkm
	https://doi.org/10.29007/x4p4

BibTeX entry

@inproceedings{CAINE2020:Image_Processing_High_Throughput,
  author    = {Venkat Margapuri and Chaney Courtney and Mitchell Neilsen},
  title     = {Image Processing for High-Throughput Phenotyping of Seeds},
  booktitle = {CAINE 2020. The 33rd International Conference on Computer Applications  in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Takaaki Goto and Quan Yuan},
  series    = {EPiC Series in Computing},
  volume    = {75},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/RQkm},
  doi       = {10.29007/x4p4},
  pages     = {69-79},
  year      = {2021}}

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