A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment

Abstract

Waste collection, classifcation, and planning have become crucial as industrialization and smart city advancement activities have increased. A recycling process of waste relies on the ability to retrieve the characteristics as it was in their natural position, and it reduces pollution and helps in a sustainable environment. Recently, deep learning (DL) methods have been employed intelligently to support the administration’s strategized waste manage ment and related procedure, including capture, classifcation, composting, and dumping. The selection of the optimum DL technique for categorizing and forecasting waste is a long and arduous process. This research presents a smart waste classifcation using Hybrid CNN-LSTM with transfer learning for sustainable development. The waste can be classi fed into recyclable and organic categories. To classify waste statistics, implement a hybrid model combining Convolutional neural networks (CNN) and long short-term memory (LSTM). The proposed model also uses the transfer learning (TL) method, which incorpo rates the advantage of ImageNet, to classify and forecast the waste category. The proposed model also utilises an improved data augmentation process for overftting and data sam pling issues. An experimental analysis was conducted on the TrashNet dataset sample, with 27027 images separated into two classes of organic waste 17005 and recyclable waste 10 025 used to evaluate the performance of the proposed model. The proposed hybrid model and various existing CNN models (i.e., VGG-16, ResNet-34, ResNet-50, and AlexNet) were implemented using Python and tested based on performance measuring parameters, i.e., precision, recall, testing and training loss, and accuracy. Each model was created with a range of epochs and an adaptive moment estimator (AME) optimisation algorithm. For the proposed method, the AME optimisation achieved the best optimisation and accuracy and the least modelling loss for training, validation, and testing. The proposed model per formed the highest precision of 95.45%, far better than the existing deep learning method.