IndianFoodNet-30

About IndianFoodNet-30

IndianFoodNet-30 is created by Ritu Agarwal, Nikunj Bansal, Tanupriya Choudhury, Tanmay Sarkar & Neelu Jyothi Ahuja with a goal of building an Indian Food detection model. It contains more than 5500 images of 30 popular Indian food items.

Data collection

We used search engines (Google and Bing) to crawl and look for suitable images using JavaScript queries for each food item from the list created. The images with incomplete RGB channels were removed, and the images collected from different search engines were compiled. When downloading images from search engines, many images were irrelevant to the purpose, especially the ones with a lot of text in them. We deployed the EAST text detector to segregate such images. Finally, a comprehensive manual inspection was conducted to ensure the relevancy of images in the dataset.

Fair use

This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. If you wish to use copyrighted material in our dataset for purposes of your own that go beyond non-commercial research and academic purposes, you must obtain permission directly from the copyright owner. We believe this constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed without profit to those who have expressed a prior interest in receiving the included information for non-commercial research and educational purposes.(adapted from Christopher Thomas).

Citation

If you find our dataset useful, please cite us as: @dataset{dataset, author = {Agarwal, Ritu and Bansal, Nikunj and Choudhury, Tanupriya and Sarkar, Tanmay and J.Ahuja, Neelu}, year = {2023}, title = {IndianFoodNet-30 Dataset}, publisher = {Roboflow Universe}, url = {https://universe.roboflow.com/indianfoodnet/indianfoodnet}, }

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

• Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.
• Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.
• Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.
• Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).
• Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.
• Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.

Land use typologies

• Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).
• Vineyard: grape vines grown in order to produce wine or table grape.
• Olive groves: olive trees grown in order to produce olive oil or table olives.
• Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.
• Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.
• Nurseries: ornamental plants, trees, flowers.

Credit

Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846

https://www.mdpi.com/2071-1050/11/7/1846

IndianFood-7

About IndianFood-7

IndianFood-7 is created by Ritu Agarwal, Nikunj Bansal, Tanmay Sarkar, Tanupriya Choudhury and Neelu Jyothi Ahuja with a goal of building a Indian Food detection model. It contains more than 800 images of 7 popular Indian food items.

Data collection

We used search engines (Google and Bing) to crawl and look for suitable images using JavaScript queries for each food item from the list created. The images with incomplete RGB channels were removed, and the images collected from different search engines were compiled. When downloading images from search engines, many images were irrelevant to the purpose, especially the ones with a lot of text in them. We deployed the EAST text detector to segregate such images. Finally, a comprehensive manual inspection was conducted to ensure the relevancy of images in the dataset.

Fair use

This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. If you wish to use copyrighted material in our dataset for purposes of your own that go beyond non-commercial research and academic purposes, you must obtain permission directly from the copyright owner. We believe this constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed without profit to those who have expressed a prior interest in receiving the included information for non-commercial research and educational purposes.(adapted from Christopher Thomas).