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.

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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

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