Machine Learning Based Massive Leaf Falling Detection For Managing The Waste Disposal Efficiently

Authors

  • Dr. Buli Yohannis Tasisa
  • Dr. Leta Tesfaye Jule
  • Dr. V. Saravanan
  • Dr.P.John Augusitne
  • Dr. G. Karthikeyan
  • Dr. Madiajagan M

Keywords:

Leaf fall detection, Machine Learning, Detection, Feature extraction

Abstract

 Massive falling of leaves in dense tree region during autumn environment often creates huge collection of waste each year. To maintain and manage a clean environment, it is necessary to collect the dried leaf waste regularly at rapid intervals. In order of claiming this, it is very essential to development a leaf falling simulator using objective detection principle that measures the size of the leaf, color and other relevant features for efficient feature selection, detection and collection of waste. In order to accommodate these three tasks, the study uses a machine learning detection that essentially identifies the leaves based on input datasets. The study considers various input features like size of the leaf, color, falling rate of a dried leaf and moment of inertia. These features are utilized for detecting the falling leaves and providing the input for clearing the leaf waste in that region. The experiments are conducted to test the real-time applicability of the model against various trees and in different regions.

References

Desbenoit, B., Galin, E., Akkouche, S., &Grosjean, J. (2006). Modeling Autumn Sceneries. In Eurographics (Short Presentations) (pp. 107-110).

Ghanshala, T., Tripathi, V., & Pant, B. (2020, November). A Machine Learning Based Framework for Intelligent High Density Garbage Area Classification. In Proceedings of the Future Technologies Conference (pp. 147-152). Springer, Cham.

De Carolis, B., Ladogana, F., &Macchiarulo, N. (2020, May). YOLO TrashNet: Garbage Detection in Video Streams. In 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) (pp. 1-7). IEEE.

Wu, Z., Liu, Y., Ma, J., &Guo, K. (2019). The Research and Countermeasures of Garbage classification in University-Case Study of Tianjin Agricultural University. DEStech Transactions on Social Science, Education and Human Science, (esem).

Donati, L., Fontanini, T., Tagliaferri, F., &Prati, A. (2020). An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection. Applied Sciences, 10(22), 8146.

Meng, C. Y., & Sheng, Y. D. (2019). Design of Small Lawn Garbage Sweeper. In MATEC Web of Conferences (Vol. 256, p. 02017). EDP Sciences.

Kang, Z., Yang, J., Li, G., & Zhang, Z. (2020). An Automatic Garbage Classification System Based on Deep Learning. IEEE Access, 8, 140019-140029.

Li, X., Bi, F., Han, Z., Qin, Y., Wang, H., & Wu, W. (2019). Garbage source classification performance, impact factor, and management strategy in rural areas of China: A case study in Hangzhou. Waste Management, 89, 313-321.

Berthier, H. C. (2003). Garbage, work and society. Resources, Conservation and Recycling, 39(3), 193-210.

Li, X., Bi, F., Han, Z., Qin, Y., Wang, H., & Wu, W. (2019). Garbage source classification performance, impact factor, and management strategy in rural areas of China: A case study in Hangzhou. Waste Management, 89, 313-321.

Liu, C. L., Lee, C. H., & Lin, P. M. (2010). A fall detection system using k-nearest neighbor classifier. Expert systems with applications, 37(10), 7174-7181.

Jemilda, G., &Baulkani, S. (2018). Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine. International Journal of Computers, Communications & Control, 13(2).

Bazi, Y., &Melgani, F. (2018). Convolutional SVM networks for object detection in UAV imagery. Ieee transactions on geoscience and remote sensing, 56(6), 3107-3118.

Kousik, N., Natarajan, Y., Raja, R. A., Kallam, S., Patan, R., &Gandomi, A. H. (2020). Improved Salient Object Detection Using Hybrid Convolution Recurrent Neural Network, Expert Systems with Applications, Vol 166, 114064, 2021.

Yuvaraj N, Pragash and Karthikeyan, “An Improved Task Allocation Scheme in Serverless Computing using Gray Wolf Optimization (GWO) based Reinforcement Learning (RIL) Approach”, Wireless Personal Communication, 2020. https://doi.org/10.1007/s11277-020-07981-0

Yuvaraj, N., Suresh Ghana Dhas, C. High-performance link-based cluster ensemble approach for categorical data clustering. J Supercomput 76, 4556–4579 (2020). https://doi.org/10.1007/s11227-018-2526-z

Daniel A., BharathiKannan B., Yuvaraj N., Kousik N.V. (2021) Predicting Energy Demands Constructed on Ensemble of Classifiers. In: Dash S.S., Das S., Panigrahi

B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15- 5566-4_52

Carnero, M. C. (2020). Waste segregation FMEA model integrating intuitionistic fuzzy set and the PAPRIKA method. Mathematics, 8(8), 1375.

Chen, S., Huang, J., Xiao, T., Gao, J., Bai, J., Luo, W., & Dong, B. (2020). Carbon emissions under different domestic waste treatment modes induced by garbage classification: Case study in pilot communities in Shanghai, China. Science of The Total Environment, 717, 137193.

Lou, C. X., Shuai, J., Luo, L., & Li, H. (2020). Optimal transportation planning of classified domestic garbage based on map distance. Journal of environmental management, 254, 109781.

Sohag, M. U., &Podder, A. K. (2020). Smart garbage management system for a sustainable urban life: An IoT based application. Internet of Things, 11, 100255.

Chen, Y., Nakazawa, J., Yonezawa, T., &Tokuda, H. (2019). Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks. Ad Hoc Networks, 85, 32-45.

Vogler, S., & de Rooij, R. H. (2018). Medication wasted–Contents and costs of medicines ending up in household garbage. Research in Social and Administrative Pharmacy, 14(12), 1140-1146.

Downloads

Published

2021-02-28

How to Cite

Tasisa, D. B. Y. ., Jule, D. L. T. ., Saravanan, D. V. ., Augusitne, D. ., Karthikeyan, D. G. ., & M, D. M. . (2021). Machine Learning Based Massive Leaf Falling Detection For Managing The Waste Disposal Efficiently. The Journal of Contemporary Issues in Business and Government, 27(1), 288–297. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/559

Issue

Vol. 27 No. 1 (2021): The journal of contemporary issues in business and government

Section

Articles

License

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.