Dataset created under the PixelCropRobot project, developed by FCUP, INESC TEC and FEUP. Dataset folder: This folder contains the images of each species in two formats (3456 × 4608 pixels and 864 × 1152 pixels), the annotations of the 864 × 1152 px. images, in Pascal VOC (.xml) and YOLO (.txt) formats and also a set of Python scripts useful for managing the dataset. The aim was to capture images of eight crops selected taking into account the length of the crop cycle (annual), the intensity of agricultural practices (mainly weed removal) and the low impact of pests and diseases. The images were captured using a smartphone (Huawei Mate 10 Lite), with 16 megapixels (MP) resolution (3456 × 4608 px.), in Professional mode (no flash, continuous autofocus, automatic ISO and shutter speed). Image collection took place at different hours of the day, with variable lighting conditions. The images are divided as follows (in parenthesis are the classes): Arugula - 312 (coty, minus9, plus9) Carrot - 533 (coty, smallleaves, carrot) Coriander - 321 (coty, smallleaves, coriander) Lettuce - 1426 (coty, minus9, plus9, ready) Radish - 494 (coty, smallleaves, bigleaves, root) Spinach - 270 (spinach, big) Swiss chard - 454 (coty, chard) Turnip - 313 (coty, smallleaves, turnip) To standardise the dataset, each image was renamed according to the corresponding EPPO (European and Mediterranean Plant Protection Organization) code and the date of creation of that image. The size of each image was also reduced four times (to 864 × 1152 pixels) to facilitate processing. For example, an image of lettuce captured on June 22 presents the name as follows: LACSA_Jun_22_x_864_1152.jpg.

{"references": ["Rodrigues, L.; Magalh\u00e3es, S.A.; da Silva, D.Q.; dos Santos, F.N.; Cunha, M. Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops. Agronomy 2023, 13, 463. https://doi.org/10.3390/agronomy13020463"]}