A dynamic, real-time algorithm for seed counting

Mitchell L. Neilsen; Chaney Courtney; Siddharth Amaravadi; Zhiqiang Xiong; Jesse Poland; Trevor Rife

A dynamic, real-time algorithm for seed counting

Mitchell L. Neilsen, Chaney Courtney, Siddharth Amaravadi, Zhiqiang Xiong, Jesse Poland, Trevor Rife

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Kansas State University is a world leader in the study of wheat genetics to develop new varieties that can tolerate a wide range of environmental conditions. Field Book, a new mobile application, was developed to modernize plant breeding programs around the world. Building off its success, we've developed several additional mobile apps for plant breeding. As a part of this effort, novel image analysis algorithms are being developed to model and extract plant phenotypes. This paper describes a dynamic, real-time algorithm to accurately count seeds using a modest mobile device. There are many directions for future research to enhance the algorithm's performance and accuracy. The new algorithm can be used to count a wide variety of different types of crop seeds for high-throughput phenotyping.

Original language	English (US)
Title of host publication	26th International Conference on Software Engineering and Data Engineering, SEDE 2017
Publisher	The International Society for Computers and Their Applications (ISCA)[email protected]
Pages	7-12
Number of pages	6
ISBN (Print)	9781943436095
State	Published - Jan 1 2017
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

Cite this

@inproceedings{735e9a5ba0c544f7ad9e9ad0c8c81747,

title = "A dynamic, real-time algorithm for seed counting",

abstract = "Kansas State University is a world leader in the study of wheat genetics to develop new varieties that can tolerate a wide range of environmental conditions. Field Book, a new mobile application, was developed to modernize plant breeding programs around the world. Building off its success, we've developed several additional mobile apps for plant breeding. As a part of this effort, novel image analysis algorithms are being developed to model and extract plant phenotypes. This paper describes a dynamic, real-time algorithm to accurately count seeds using a modest mobile device. There are many directions for future research to enhance the algorithm's performance and accuracy. The new algorithm can be used to count a wide variety of different types of crop seeds for high-throughput phenotyping.",

author = "Neilsen, {Mitchell L.} and Chaney Courtney and Siddharth Amaravadi and Zhiqiang Xiong and Jesse Poland and Trevor Rife",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-13",

year = "2017",

month = jan,

day = "1",

language = "English (US)",

isbn = "9781943436095",

pages = "7--12",

booktitle = "26th International Conference on Software Engineering and Data Engineering, SEDE 2017",

publisher = "The International Society for Computers and Their Applications (ISCA)[email protected]",

}

A dynamic, real-time algorithm for seed counting. / Neilsen, Mitchell L.; Courtney, Chaney; Amaravadi, Siddharth et al.
26th International Conference on Software Engineering and Data Engineering, SEDE 2017. The International Society for Computers and Their Applications (ISCA)[email protected], 2017. p. 7-12.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A dynamic, real-time algorithm for seed counting

AU - Neilsen, Mitchell L.

AU - Courtney, Chaney

AU - Amaravadi, Siddharth

AU - Xiong, Zhiqiang

AU - Poland, Jesse

AU - Rife, Trevor

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Kansas State University is a world leader in the study of wheat genetics to develop new varieties that can tolerate a wide range of environmental conditions. Field Book, a new mobile application, was developed to modernize plant breeding programs around the world. Building off its success, we've developed several additional mobile apps for plant breeding. As a part of this effort, novel image analysis algorithms are being developed to model and extract plant phenotypes. This paper describes a dynamic, real-time algorithm to accurately count seeds using a modest mobile device. There are many directions for future research to enhance the algorithm's performance and accuracy. The new algorithm can be used to count a wide variety of different types of crop seeds for high-throughput phenotyping.

AB - Kansas State University is a world leader in the study of wheat genetics to develop new varieties that can tolerate a wide range of environmental conditions. Field Book, a new mobile application, was developed to modernize plant breeding programs around the world. Building off its success, we've developed several additional mobile apps for plant breeding. As a part of this effort, novel image analysis algorithms are being developed to model and extract plant phenotypes. This paper describes a dynamic, real-time algorithm to accurately count seeds using a modest mobile device. There are many directions for future research to enhance the algorithm's performance and accuracy. The new algorithm can be used to count a wide variety of different types of crop seeds for high-throughput phenotyping.

UR - http://www.scopus.com/inward/record.url?scp=85031699632&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781943436095

SP - 7

EP - 12

BT - 26th International Conference on Software Engineering and Data Engineering, SEDE 2017

PB - The International Society for Computers and Their Applications (ISCA)[email protected]

ER -

A dynamic, real-time algorithm for seed counting

Abstract

Bibliographical note

Other files and links

Fingerprint

Cite this