CloudBridge Waste Segregator Automation using Machine Learning
Kavyashree M K1, Arpitha G R2, Kavya R3, Surabhi C4, Phi I Banrisha L R5
1Kavyashree M K , assistant professor, Department of Electronics and Communication Engineering. Mysore. (Karnataka), India.
2Arpitha G R, Bachelor of Engineering Department of Electronics and Communication from Sri Jayachamarajendra College of Engineering. Mysore. (Karnataka), India.
3Kavya R, Bachelor of Engineering, Department of Electronics and Communication Engineering from Sri Jayachamarajendra College of Engineering. Mysore. (Karnataka), India.
4Surabhi C, B.E graduate student, Department of Electronics & Communication Engineering from Sri Jayachamarajendra College of Engineering, Mysore. (Karnataka), India.
5Banrisha L R, B.E graduate student from the Department of Electronics and Communication Engineering from Sri Jayachamarajendra College of Engineering, Mysore. (Karnataka), India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2794-2798 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8511078919/19©BEIESP | DOI: 10.35940/ijitee.I8511.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There is a huge problem in creating space today because of growing population and research is going on profusely in finding space to dump waste. The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. The framework used to robotize the procedure of waste isolation to deal with the junk effectively and productively is one of the Machine Learning strategies called Convolutional Neural Network (CNN). The experiments showed that the performance of CNN is better because it recognizes the components in an image and recombines these components to recognize other structures while other methods learn to recognize as they go through it. The work will be segregated into 6 bins consisting of biodegradable, non- biodegradable. Here we have used the TensorFlow algorithm which uses Python. The applications of TensorFlow are Python application itself. The application of our research includes waste segregation in society, in industries, in agricultural fields. The recycled wastes can be used as organic material in many places.
Keywords: TensorFlow, Convolutional Neural Networks, Arduino, Python, Machine Learning.
Scope of the Article: Machine Learning