Update README.md

README.md

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 This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. 
 To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. 
 
-Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color
+Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color.
 
 🤗 [Paper on Hugging Face](https://huggingface.co/papers/2505.08537)   |   📝 [Paper on ArXiv](https://arxiv.org/abs/2505.08537)
 
@@ -85,13 +85,10 @@ Please refer to [this page](https://docs.ultralytics.com/datasets/segment/) for
 
 ## 📖 Citation
 ```bibtex
-@misc{mekhalfi2025raspgradedatasetautomaticraspberry,
-      title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning}, 
-      author={Mohamed Lamine Mekhalfi and Paul Chippendale and Fabio Poiesi and Samuele Bonecher and Gilberto Osler and Nicola Zancanella},
-      year={2025},
-      eprint={2505.08537},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2505.08537}, 
+@article{mekhalfi2025raspgrade,
+  title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning},
+  author={Mekhalfi, Mohamed Lamine and Chippendale, Paul and Poiesi, Fabio and Bonecher, Samuele and Osler, Gilberto and Zancanella, Nicola},
+  journal={arXiv preprint arXiv:2505.08537},
+  year={2025}
 }
 ```