Complete good and bad Computer Vision Project

MilanR

Updated a year ago

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Classes (30)
apple_1
aubergine_1
bad-quality-apple
bad-quality-banana
bad-quality-lemon
bad-quality-pepper
bag
banana_1
bloom_1
capsicium_pepper_1
carrot_1
cucumber_1
damage
eggplant_1
grapefruit
large_lemon
lemon_1
melon object
onion_1
orange_1
passion_fruit_1
pear_1
pepper_1
potato_1
pumpkin_1
small_lemon
tomato_1
tomato_bunch
zucchini_1
Description

Here are a few use cases for this project:

  1. Grocery Store Inventory check: The model can be used in grocery stores to automatically and continually check the quality and quantity of fruits and vegetables. It can segment 'good' and 'bad' produce, ensuring stores always offer fresh supplies.

  2. Automated Sorting: In agricultural businesses, this computer vision model can facilitate automated sorting systems to discriminate between good quality fruits and vegetables and those of poor quality. This can speed up the sorting process and lower the chance of bad produce getting into the market.

  3. Greenhouse Monitoring: Use of the model in a greenhouse setting can help farmers to quickly identify and remove rotten vegetables and fruits, enhancing the overall health of the farm and quality of produce.

  4. Food Waste Management: In waste management and recycling centers, the model can be used to identify rotten fruits and vegetables, aiding in composting and biogas production processes based on the types of organic waste.

  5. Smart Refrigerators: In a smart home setting, this computer vision model can be used in the refrigerator to alert the users when their stored fruits or vegetables are becoming bad, ensuring efficient use of grocery items and reducing food waste.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            complete-good-and-bad_dataset,
                            title = { Complete good and bad Dataset },
                            type = { Open Source Dataset },
                            author = { MilanR },
                            howpublished = { \url{ https://universe.roboflow.com/milanr/complete-good-and-bad } },
                            url = { https://universe.roboflow.com/milanr/complete-good-and-bad },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-11-19 },
                            }
                        
                    

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