Real-Time Tree Counting Android Application and Central Monitoring System
Автор: Ahmet Ali Süzen, Remzi Gürfidan, Kıyas Kayaalp, Mehmet Ali Şimşek
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 2 Vol. 12, 2020 года.
Бесплатный доступ
In this study, a cloud-based android application and centralized tracking software were developed to perform an accurate and uninterrupted tree count across open lands. The application is used to count the desired number of trees and species at the same time. User-logged data and location information are saved in real-time to the application's cloud database. The application can work online and with offline mod. In cases where there is no internet connection, it inserts the data to the local SQLite database. After the connection is established, the pairing continues. It's used Google Firebase on the cloud server for data storage. The processing of target locations and GPS coordinates was developed with the Google Map Library. The tree counting application automatically picks up the user's current location when it is first opened. The counting starts after the tree and tree species that the user has selected from the menu. The software developed shows that tree counting is done simultaneously at the desired point. It also solves confusion caused by different tree species during the counting. We've received feedback from 100 people using the application. The users answered five questions. As a result, it is aimed to provide a comfortable transition between tree species and its users with its simple use to eliminate the complexity of counting and save time.
Android, Cloud System, Firebase, Location based tracking, Tree count
Короткий адрес: https://sciup.org/15017443
IDR: 15017443 | DOI: 10.5815/ijitcs.2020.02.02
Список литературы Real-Time Tree Counting Android Application and Central Monitoring System
- Çiftçi, T. “Tarım Sigortalarının Devlet Tarafından Desteklenmesi ve Tarım Sigortaları Havuzu Sistemi”. Ankara Barosu Dergisi, (4) ,2014.
- Doğan, Z., Arslan, S., & Berkman, A. “Türkiye’de tarım sektörünün iktisadi gelişimi ve sorunları: tarihsel bir bakış”. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 29-41, 2015.
- Ertan, A., & Mustafa, G. Ö. K. “Eğirdir İlçesi Tarım Üreticilerinin Tarım Sigortası Yaptırmaya Karar Verme Sürecinde Etkili Olan Faktörlerin Analizi”. ODÜ Sosyal Bilimler Araştırmaları Dergisi (ODÜSOBİAD), 3(5), 66-76, 2012.
- Li, W., Fu, H., Yu, L., & Cracknell, A. “Deep learning based oil palm tree detection and counting for high-resolution remote sensing images.” Remote Sensing, 9(1), 22, 2017.
- Dorj, U. O., Lee, M., & Yun, S. S., “An yield estimation in citrus orchards via fruit detection and counting using image processing”. Computers and Electronics in Agriculture, 140, 103-112, 2017.
- Sotoca, J. M., Mollineda, R. A., & Sánchez, J. S. “A meta-learning framework for pattern classication by means of data complexity measures”. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 10(29), 31-38, 2016.
- Bresilla, K., Perulli, G. D., Boini, A., Morandi, B., Grappadelli, L. C., & Manfrini, L. “Single-shot convolution neural networks for real-time fruit detection within the tree”. Frontiers in plant science, 10, 2019.
- Fiona, J. R., & Anitha, J., “Automated Detection of Plant diseases and Crop Analysis in Agriculture using Image Processing Techniques: A Survey”. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE, February 2019.
- Ünal, K., & Kurucu, Y., “Zeytin Ağaç Sayımında Yüksek Çözünürlüklü Uydu Görüntülerinin Kullanımı”. Zeytin Bilimi, 2(1), 1-11, 2011.
- Aydoğdu, M., Güzel, A., Sürücü, A., Aydoğdu, M. H., & Çullu, M. A., “Tektek Platolari Antepfistiği Florasinin Ve Ağaç Sayilarinin Uzaktan Algilama Ve Coğrafi Bilgi Sistemleri Teknikleri Kullanilarak Belirlenmesi” . Electronic Turkish Studies, 8(12), 2013.
- Santoro, F., Tarantino, E., Figorito, B., Gualano, S., & D'Onghia, A. M., “A tree counting algorithm for precision agriculture tasks”. International Journal of Digital Earth, 6(1), 94-102,2013.
- Bazi, Y., Malek, S., Alajlan, N., & AlHichri, H., “An automatic approach for palm tree counting in UAV images”. In 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 537-540). IEEE,2014.
- Li, W., Fu, H., Yu, L., & Cracknell, A., “Deep learning based oil palm tree detection and counting for high-resolution remote sensing images”. Remote Sensing, 9(1), 22, 2017.
- Wu, J., Ping, L., Ge, X., Wang, Y., & Fu, J., “Cloud storage as the infrastructure of cloud computing”. In 2010 International Conference on Intelligent Computing and Cognitive Informatics (pp. 380-383). IEEE, June, 2010.
- Makhlouf, S. A., & Yagoubi, B., “Clustering Strategy for Scientific Workflow Applications in IaaS Cloud Environment”. In International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning (pp. 482-491). Springer, Cham. October, 2018.
- Moroney, L., “The firebase real-time database”. In The Definitive Guide to Firebase (pp. 51-71). Apress, Berkeley, CA, 2017.
- Chatterjee, N., Chakraborty, S., Decosta, A., & Nath, A., “Real-time Communication Application Based on Android Using Google Firebase”. Int. J. Adv. Res. Comput. Sci. Manag. Stud, 2018.
- Lahudkar, P., Sawale, S., Deshmane, V., & Bharambe, K., “NoSQL Database-Google’s Firebase: A Review”. International Journal of Innovative Research in Science, Engineering and Technology, 7(3), 2018.
- Khedkar, S., Thube, S., Estate, W. I., & Naka, C., “Real time databases for applications”. International Research Journal of Engineering and Technology (IRJET), 4(06), 2078-2082, 2017.
- Aksoy, B., “Three dimensional online virtual apparel internet page application”. Industria Textila, 67(4), 256–259, 2017.
