Analyzing and Categorizing Waste Using Convolutional Neural Networks and TensorFlow

In the realm of environmental challenges, waste management emerges as a critical concern, sparking a demand for innovative solutions to boost sustain ability efforts. In this study, an innovative approach to waste classification is introduced, harnessing Convolutional Neural Networks paired with TensorFlow, a robust open-source machine learning platform known for its flexibility and efficiency in model development and deployment. The study aims to address the essential task of automating waste classification, a key step towards streamlining recycling processes and reducing environmental impacts. The methodology delves into detailed analyses of the CNN architecture, preprocessing techniques and hyperparameter optimization. The research includes a thorough evaluation of model performance across various metrics such as accuracy, block size, dropouts, loss and time required for epochs, showcasing the resilience and adaptability of CNN and TensorFlow in precisely categorizing a range of waste materials, spanning from batteries, cardboard, metal, plastics, glasses, and paper. By leveraging deep learning technologies, the proposed methodology demonstrates promising outcomes in waste classification, paving the way for advancements in waste management practices on a larger scale. This contribution to the field of environmental technology offers a dependable framework for waste classification, aligning with the overarching goal of promoting a cleaner and more sustainable planet. Furthermore, the utilization of the deep CNN model shows potential for accurate waste identification, enhancing the efficiency of garbage classification processes and potentially enabling real-time waste detection. Noteworthy is the preference for EfficientNetB0 over Transfer-Learning alternatives such as DenseNet201, ResNet50 and InceptionV3, boasting superior performance accuracy at 94.15% alongside reduced parameter complexity.