Technical solutions for waste classification and management: A mini-review

Abstract

The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission.

Keywords: Waste management; communication protocols; convolutional neural networks; internet of things; sensors; waste segregation.

Figure 1.

Waste segregation according to solid waste management rules 2016 (Teachoo, 2020).

Figure 2.

Example of CNN architecture (Kundathil, 2020). CNN: convolutional neural network.

Figure 3.

Sample images from each class of TrashNet dataset.