canola_detection_dataset.zip

RGB images were obtained from three distinct datasets featuring canola in its early growth stage intermingled with weeds, including <b>T1 Miling</b> (T1_miling), <b>T2 Miling</b> (T2_Miling), and <b>York canola</b> (YC). These datasets were collected in Western Australia from distances ranging between 0.5 meters and 1.5 meters, captured at various angles. The images are processed as 500 by 500 pixel frames, comprising the core images along with their corresponding segmentation masks. Manual delineation of bounding boxes using the polygon tool was performed using the Make Sense annotation tool by Skalski, P. [GitHub: https://github.com/SkalskiP/make-sense/].For T1 and T2 Millet datasets, the images showcase canola plants in conjunction with rye grass, whereas the York canola dataset features images of canola alongside regrowth of blue lupin. Image collection involved the use of a smartphone for T1 and T2 and a Canon 600D DSLR camera for YC. If you are interested in the scripts for image processing and deep learning, they can be accessed at [GitHub: https://github.com/mikemcka/Canola_detection_dl/tree/main].In this repository you will find the RGB images and weed-crop-soil segmentation masks obtained using CIVE vegetation index.