Weed Remover Operation Tracking Dataset

The current agricultural sector faces challenges such as low weeding efficiency and high labor costs. Traditional weeding methods rely on manual operations that are difficult to automate. Existing solutions like mechanical weed removers have made improvements but still lack precise object detection capabilities, resulting in low operational efficiency. This dataset aims to provide high-quality operation image data of weed removers to support the training of object detection algorithms, thereby enhancing the automation level of weeding operations in smart agriculture. During data collection, high-resolution cameras were used to capture real-time images of weed remover operations in various agricultural environments, ensuring coverage of different lighting conditions, weather, and crop types. To ensure data quality, multiple rounds of annotation and expert review procedures were implemented to ensure consistency and accuracy of the data. Data is stored in JPG format, organized by directory structure for efficient access and processing.