Due to aggressive urbanization (with population size), waste increases exponentially, resulting in environmental damage. Even though it looks challenging, such an issue can be controlled if we can reuse them. To handle this, in our work, we design a machine learning and blockchain-oriented system that identifies the waste objects/products and recommends to the user multiple ‘Do-It-Yourself’ (DIY) ideas to reuse or recycle. Blockchain records every transaction in the shared ledger to enable transaction verifiability and supports better decision-making. In this study, a Deep Neural Network (DNN) trained on about 11700 images is developed using ResNet50 architecture for object recognition (training accuracy of 94%). We deploy several smart contracts in the Hyperledger Fabric (HF) blockchain platform to validate recommended DIY ideas by blockchain network members. HF is a decentralized ledger technology platform that executes the deployed smart contracts in a secured Docker container to initialize and manage the ledger state. The complete model is delivered on a web platform using Flask, where our recommendation system works on a web scraping script written using Python. Fetching DIY ideas using web-scraping takes nearly 1 second on a desktop machine with an Intel Core-i7 processor with 8 cores, 16 GB RAM, installed with Ubuntu 18.04 64-bit operating system, and Python 3.6 package. Further, we evaluate blockchain-based smart contracts’ latencies and throughput performances using the hyperledger caliper benchmark. To the best of our knowledge, this is the first work that integrates blockchain technology and deep learning for the DIY recommender system.
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