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README.md

@@ -31,4 +31,36 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- object-detection
+size_categories:
+- n<1K
 ---
+# Greenhouse Tomato Agrob Object Detection
+
+AgRobTomato is a greenhouse dataset of 'Plum' variety tomato plants captured by the AgRob v16 mobile robot as it was driven along the inter-rows of a commercial greenhouse in Barroselas, Portugal, in August 2020, as part of the ROBOCARE robotic-harvesting project. Frames were sampled every 3 seconds from the robot's ZED camera video, and every tomato fruit is annotated with a bounding box labelled with one of four ripeness stages from the USDA colour chart: unripe (green), breaking, reddish, and ripe. Most fruits are unripe and many are partly hidden by leaves, so each box also carries an occlusion flag. The dataset contains 449 images with 6,084 bounding box annotations across 4 categories.
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{moreira2022benchmark,
+  title={Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato},
+  author={Moreira, Germano and Magalhães, Sandro Augusto and Pinho, Tatiana and dos Santos, Filipe Neves and Cunha, Mário},
+  journal={Agronomy},
+  volume={12},
+  pages={356},
+  year={2022},
+  publisher={MDPI}
+}
+```
+
+
+The dataset itself can be cited as:
+
+Magalhães, S. A., Moreira, G., dos Santos, F. N., &amp; Cunha, M. (2021). *AgRobTomato Dataset: Greenhouse tomatoes with different ripeness stages* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5596799
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*