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

Ramesh Ajikaran, Ashen Iranga Hewarathna, Vigneshwaran Palanisamy, Charles Joseph, Selvarajah Thuseethan

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE 17th International Conference on Industrial and Information Systems, ICIIS 2023 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages25-30
Number of pages6
ISBN (Electronic)9798350323634
DOIs
Publication statusPublished - 2023
Event17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023 - Hybrid, Peradeniya, Sri Lanka
Duration: 25 Aug 202326 Aug 2023

Publication series

Name2023 IEEE 17th International Conference on Industrial and Information Systems, ICIIS 2023 - Proceedings

Conference

Conference17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023
Country/TerritorySri Lanka
CityHybrid, Peradeniya
Period25/08/2326/08/23

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