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Vol.13, No.3, August 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Mobile Web Application for Durian Orchard Management and Geospatial Data Visualization Using Deep Learning
Supattra Puttinaovarat, Aekarat Saeliw, Jinda Kongcharoen, Siwipa Pruitikanee, Pimlaphat Pengthorn, Athicha Ketkaew, Kanit Khaimook
© 2024 Supattra Puttinaovarat, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 3, Pages 1837-1848, ISSN 2217-8309, DOI: 10.18421/TEM133-12, August 2024.
Received: 24 April 2024. Revised: 01 July 2024.
Abstract:
Durian, a globally popular fruit, is primarily exported by Thailand, making it the foremost contributor to the world market. Nevertheless, there remains a notable absence of a comprehensive platform or application catering to both consumer tourists and businesses seeking domestic purchases. Prior research has highlighted several shortcomings, notably the inability of existing applications to provide location-based search functionality or automatically identify durian plantation plots from digital photographs. Consequently, this study proposes the development of a mobile web application aimed at managing, processing, and visualizing geospatial data pertaining to orchards and durian plantations. Through the integration of mobile technology, geospatial technology, and machine learning, the research endeavours to address these deficiencies. The findings indicate promising results, particularly in the accurate classification of durian plantations using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest, and k-nearest neighbor (KNN). Among these algorithms, CNN exhibited the highest accuracy, achieving a value of 95%, with precision, recall, and f-measure values of 95.55%, 94.44%, and 94.97%, respectively.
Keywords – Durian plantation classification, deep learning, geospatial data visualization. |
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