An extensive photographic dataset to classify laptop components for automating e-waste management by recycling old laptops

Automatic electronic waste management needs to classify components specifically from laptops of different models and brands so that parts can be recycled efficiently using AI-enabled robots. To achieve this goal, a good dataset plays a significant role as the systems that operate e waste management machines will learn from this dataset and act accordingly. The lack of proper datasets that are available publicly related to components of any type of device can be a barrier to the work process. This research aims to improve the scenario by developing, to the best of knowledge, the first-ever publicly available and ready-to-use standard dataset of laptop components. Most of the images are collected from three different computer firms. The dataset consists of 29,120 images of 26 different components. The dataset is developed using components of laptops from eight models of HP brand and it deals with the common components that can be found in any laptops of any brand. This dataset is anticipated to have a significant impact on the field of automated recycling as it is expected to attract wide attention from machine-learning researchers and practitioners in this specified field.