Deep Learning Based Smart Garbage Monitoring System

Padidela Swarochish Rao, S. Rao, R. Ranjan
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引用次数: 5

Abstract

India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins.
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基于深度学习的智能垃圾监测系统
在过去的20年里,印度的垃圾水平出现了前所未有的增长。每天都会产生大量废物,特别是固体废物,但很少被收集起来。因此,垃圾被倾倒在堆填区和水体中,因此没有得到有效管理。这种管理不善对我们的环境造成了有害的后果。因此,有必要发展一个有效的系统来管理废物。本文提出了一种基于物联网的自动化智能垃圾箱监控系统。此外,使用深度学习模型从收集的数据中预测未来的垃圾水平。所提出的神经网络模型能够以80.33%的准确率预测垃圾水平。结果验证了垃圾水平预测的准确性。此外,使用柱状图分析数据。物联网和深度学习的融合可以带来革命性的技术变革,并应用于废物管理。因此,预测和检查垃圾水平可以帮助市政当局建立一个有效的垃圾管理系统,减少垃圾箱的溢出。
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