Peruvian-Cotton - Annotated Dataset of Gossypium barbadense Fruits in Peruvian Crops

This dataset contains images and annotations used in the study “Automated Detection and Counting of Gossypium barbadense Fruits in Peruvian Crops Using Convolutional Neural Networks.” When using this dataset, please cite: Ballena-Ruiz, J.; Arcila-Diaz, J.; Tuesta-Monteza, V. Automated Detection and Counting of Gossypium barbadense Fruits in Peruvian Crops Using Convolutional Neural Networks. AgriEngineering 2025, 7, 152. https://doi.org/10.3390/agriengineering7050152 Data collection was carried out in two cotton crop plots located in the district of Lambayeque, province of Lambayeque, Lambayeque region, Peru (6°40′20″ S, 79°53′17″ W). A total of 370 original images were captured in JPG format at a resolution of 640 × 640 pixels, using a Samsung A12 device mounted on a 2.5 m-high structure. Out of these, 100 images were selected for manual annotation using LabelImg (v1.8.6), resulting in 3062 YOLO-format annotations corresponding to the class “cotton”. Subsequently, data augmentation techniques were applied (progressive rotations ranging from −5° to +5°), which expanded the dataset to 2186 images with a total of 70,348 annotated cotton fruits. This dataset is intended to support research in computer vision, object detection, and agricultural applications based on artificial intelligence.本数据集包含用于《基于卷积神经网络（Convolutional Neural Networks）自动检测与计数秘鲁农田海岛棉（Gossypium barbadense）果实》研究的图像与标注数据。 使用本数据集时，请引用如下文献： Ballena-Ruiz, J.; Arcila-Diaz, J.; Tuesta-Monteza, V. Automated Detection and Counting of Gossypium barbadense Fruits in Peruvian Crops Using Convolutional Neural Networks. AgriEngineering 2025, 7, 152. https://doi.org/10.3390/agriengineering7050152 数据采集工作开展于秘鲁兰巴耶克大区兰巴耶克省兰巴耶克区的两块棉花种植田，采集坐标为南纬6°40′20″、西经79°53′17″。本次采集共获取370张原始图像，格式为JPG，分辨率为640×640像素，采集设备为搭载于2.5米高支架上的三星（Samsung）A12手机。 其中选取100张图像使用LabelImg（v1.8.6）进行人工标注，共得到3062条对应“棉花”类别的YOLO格式标注文件。随后，通过数据增强技术（采用范围为-5°至+5°的渐进式旋转操作）对数据进行扩充，最终将数据集扩展至2186张图像，总计包含70348枚标注过的棉花果实。 本数据集旨在为计算机视觉、目标检测以及基于人工智能的农业应用相关研究提供支撑。