An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we ai...

Full description

Saved in:
Bibliographic Details
Published in:2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS) pp. 25 - 30
Main Authors: Ajikaran, Ramesh, Hewarathna, Ashen Iranga, Palanisamy, Vigneshwaran, Joseph, Charles, Thuseethan, Selvarajah
Format: Conference Proceeding
Language:English
Published: IEEE 25.08.2023
Subjects:
Deep learning
grain counting
Grain size
Manuals
Object detection
Production
smart agriculture
Tagging
Training
YOLO
ISBN:9798350323627
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.
AbstractList In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.
Author Palanisamy, Vigneshwaran
Hewarathna, Ashen Iranga
Thuseethan, Selvarajah
Ajikaran, Ramesh
Joseph, Charles
Author_xml – sequence: 1
  givenname: Ramesh
  surname: Ajikaran
  fullname: Ajikaran, Ramesh
  email: rajikaran@std.appsc.sab.ac.lk
  organization: Sabaragamuwa University of Sri Lanka,Dept. of Computing and Infromation Systems
– sequence: 2
  givenname: Ashen Iranga
  surname: Hewarathna
  fullname: Hewarathna, Ashen Iranga
  email: aihewarathna@std.appsc.sab.ac.lk
  organization: Sabaragamuwa University of Sri Lanka,Dept. of Computing and Infromation Systems
– sequence: 3
  givenname: Vigneshwaran
  surname: Palanisamy
  fullname: Palanisamy, Vigneshwaran
  email: p.vickey@appsc.sab.ac.lk
  organization: Sabaragamuwa University of Sri Lanka,Dept. of Software Engineering
– sequence: 4
  givenname: Charles
  surname: Joseph
  fullname: Joseph, Charles
  email: charles.joseph@fau.de
  organization: FAU,Dept. of Computer Science,Germany
– sequence: 5
  givenname: Selvarajah
  surname: Thuseethan
  fullname: Thuseethan, Selvarajah
  email: t.selvarajah@deakin.edu.au
  organization: School of Information Technology, Deakin University,Australia
BookMark eNpVT0tOwzAUNAIWUHoDFr5Agu3nxPYyBCiRgroA1tULfimWGqfKZ9HbEwQsWIzmo9FIc80uYh-JMS5FKqVwd1VZVa-Ztc6mSihIpVAZZODO2NoZZyEToCAHff7PK3PFtkXkVYd74kXEw2kMY3KPI3lezFPf4bSoF5o-e8_nMcQ9fyA68ppwiN-u7Qe-GTBEXvZznJbohl22eBhp_csr9v70-FY-J_V2U5VFnQQp3ZS4XLTGfTRea2O8EQtIWp9r22gEjc6RRu9ajcaAXYoqz71C2ZjGNhIIVuz2ZzcQ0e44hA6H0-7vN3wBUQNPtg
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICIIS58898.2023.10253539
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE/IET Electronic Library
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798350323634
EndPage 30
ExternalDocumentID 10253539
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i119t-960f79cbd4477d707d7e18d648b4a34a99e4ad9f4a77389cb266d2a1b7b8b13e3
IEDL.DBID RIE
ISBN 9798350323627
IngestDate Wed Jun 26 19:25:09 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i119t-960f79cbd4477d707d7e18d648b4a34a99e4ad9f4a77389cb266d2a1b7b8b13e3
PageCount 6
ParticipantIDs ieee_primary_10253539
PublicationCentury 2000
PublicationDate 2023-Aug.-25
PublicationDateYYYYMMDD 2023-08-25
PublicationDate_xml – month: 08
  year: 2023
  text: 2023-Aug.-25
  day: 25
PublicationDecade 2020
PublicationTitle 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS)
PublicationTitleAbbrev ICIIS
PublicationYear 2023
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.8420362
Snippet In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods...
SourceID ieee
SourceType Publisher
StartPage 25
SubjectTerms Deep learning
grain counting
Grain size
Manuals
Object detection
Production
smart agriculture
Tagging
Training
YOLO
Title An Image Analysis-Based Automated Method using Deep Learning for Grain Counting
URI https://ieeexplore.ieee.org/document/10253539
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NS8NAEF1s8SAIKlb8Zg9etybZ3ezusVarAa0FP-it7FdLD6alpv5-Z9dE8eDBQ2AJBMIkmfd2MvMeQheASeG1tSRPmCaMJ55IozkkQ22BkNqMR-H513sxHMrxWI3qYfU4C-O9j81nvhuW8V--W9h1KJXBF55xyqlqoZYQeTOsta2EAh6R0AySsWi6dRJ1WfSL4olLqUILV0a7zeW_jFQijgx2_nkHu6jzM5GHR99Ys4c2fLmPHnslLt4gIeBGWoRcASg53FtXC2CisHqIBtE4dLfP8LX3S1wLqs4wsFV8GwwicL_2i-igl8HNc_-O1AYJZJ6mqiKw-5gKZY1jTAgnEjh8Kl3OpGGaMq2UZ9qpKdNCADGxBtDYZTo1wkgTyp8HqF0uSn-IcE41oLWBzZENClxUGWPDbJPgqTNTzo9QJ0RjsvzSwJg0gTj-4_wJ2goxD9XXjJ-idrVa-zO0aT-q-fvqPD65T3eDltI
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELWgIIGEBIgivvHA6pLEdm2PpVAa0ZZKFMRW-atVB9KqpPx-ziEBMTAwRLIyRNElufd8uXsPoSvApPDaWtKMmCaMR55IozkkQ22BkNqEF8LzLz0xGMjXVzUsh9WLWRjvfdF85hthWfzLd3O7CqUy-MITTjlV62iDM5ZE1bjWjhIKmEREE0jHourXidR12k7TJy6lCk1cCW1UF_hlpVIgSWf3n_ewh-o_M3l4-I02-2jNZwfosZXh9A1SAq7ERcgNwJLDrVU-By4Kq35hEY1Df_sU33q_wKWk6hQDX8X3wSICt0vHiDp67tyN2l1SWiSQWRyrnMD-YyKUNY4xIZyI4PCxdE0mDdOUaaU8005NmBYCqIk1gMcu0bERRppQAD1EtWye-SOEm1QDXhvYHtmgwUWVMTZMNwkeOzPh_BjVQzTGiy8VjHEViJM_zl-ire6o3xv30sHDKdoO8Q-12ISfoVq-XPlztGk_8tn78qJ4ip-C9ZoZ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+IEEE+17th+International+Conference+on+Industrial+and+Information+Systems+%28ICIIS%29&rft.atitle=An+Image+Analysis-Based+Automated+Method+using+Deep+Learning+for+Grain+Counting&rft.au=Ajikaran%2C+Ramesh&rft.au=Hewarathna%2C+Ashen+Iranga&rft.au=Palanisamy%2C+Vigneshwaran&rft.au=Joseph%2C+Charles&rft.date=2023-08-25&rft.pub=IEEE&rft.isbn=9798350323627&rft.spage=25&rft.epage=30&rft_id=info:doi/10.1109%2FICIIS58898.2023.10253539&rft.externalDocID=10253539
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798350323627/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798350323627/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798350323627/sc.gif&client=summon&freeimage=true