Green Pepper Appearance Quality Detection Dataset

The current agricultural sector faces challenges in the appearance quality detection of agricultural products such as peppers. Traditional manual detection methods are inefficient and lack accuracy, often affected by subjective factors. Existing automated detection technologies largely rely on single image processing algorithms, making them unsuitable for varying environments and diverse quality detection needs. Therefore, the creation of this dataset aims to provide a rich sample repository to support the training of deep learning-based object detection models, enhancing automation and accuracy in detection. The dataset contains 5000 high-quality green pepper images, captured using high-resolution cameras under natural lighting conditions to ensure the authenticity and diversity of the images. Quality control of the data includes multiple rounds of annotation and consistency checks, with all annotations reviewed by experienced experts. The final data is stored in JPG format, organized by category and quality grade for easy subsequent model training and validation.