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...

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Veröffentlicht in:2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS) S. 25 - 30
Hauptverfasser: Ajikaran, Ramesh, Hewarathna, Ashen Iranga, Palanisamy, Vigneshwaran, Joseph, Charles, Thuseethan, Selvarajah
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 25.08.2023
Schlagworte:
Deep learning
grain counting
Grain size
Manuals
Object detection
Production
smart agriculture
Tagging
Training
YOLO
ISBN:9798350323627
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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
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  organization: Sabaragamuwa University of Sri Lanka,Dept. of Software Engineering
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  givenname: Charles
  surname: Joseph
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  organization: FAU,Dept. of Computer Science,Germany
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  givenname: Selvarajah
  surname: Thuseethan
  fullname: Thuseethan, Selvarajah
  email: t.selvarajah@deakin.edu.au
  organization: School of Information Technology, Deakin University,Australia
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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...
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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
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