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http://ir.lib.seu.ac.lk/handle/123456789/7335
Title: | Machine learning-based mobile application for weed detection in paddy fields |
Other Titles: | issn |
Authors: | Bhashana Ravisankha, S. E. Upeksha Hansani, K. K. Upeksha Randika, W. A. K. Kuruwitaarachchi, N. |
Keywords: | Weed Detection CNN VGG16 ResNet 50 Inception-v3 Weed Control Methods |
Issue Date: | 16-Oct-2024 |
Publisher: | Faculty of Technology, South Eastern University of Sri Lanka, Sri Lanka. |
Citation: | 4th International Conference on Science and Technology 2024 (ICST-2024) Proceedings of Papers “Exploring innovative horizons through modern technologies for a sustainable future” 16th October 2024. Faculty of Technology, South Eastern University of Sri Lanka, Sri Lanka. pp. 135-145. |
Abstract: | In the context of Sri Lanka, where agriculture, particularly paddy cultivation, plays a crucial role, farmers face significant challenges due to weed infestation. Unlike some other countries that have embraced machine learning technologies to address these issues, Sri Lanka has yet to adopt such advanced solutions. To tackle the pervasive weed problem, a research initiative was undertaken to develop a mobile application capable of identifying weed types. The methodology involved utilizing Convolutional Neural Network (CNN) pre-trained models, namely ResNet-50, Inception-v3, and VGG16, along with the Google Colab platform for training the dataset. Among the three models, VGG16 demonstrated the highest accuracy, making it the chosen model to further the research. The primary goal was to achieve a superior level of accuracy in detecting weed species in rice fields. The research team focused on delivering a mobile application with a high level of accuracy to identify and classify weeds in paddy fields. The integration of advanced technologies, such as IoT and machine learning, aimed to provide Sri Lankan farmers with an efficient and effective tool to combat weed-related challenges in their agricultural practices. |
URI: | http://ir.lib.seu.ac.lk/handle/123456789/7335 |
ISBN: | 978-955-627-028-0 |
Appears in Collections: | 4th International Conference on Science and Technology |
Files in This Item:
File | Description | Size | Format | |
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ICST_2024_Proceedings_-153-163.pdf | 420.03 kB | Adobe PDF | View/Open |
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