About Dataset (strawberries, peaches, pomegranates) Photo requirements: 1-White background 2-.jpg 3- Image size 300*300 The number of photos required is 250 photos of each fruit when it is fresh and 250 photos of each Fruit Dataset for Classification when it is rotten. Total 1500 images

Diverse Collection With a diverse collection of Product images, the files provides an excellent foundation for developing and testing machine learning models designed for image recognition and allocation. Each image is captured under different lighting conditions and backgrounds, offering a realistic challenge for algorithms to overcome.

Real-World Applications The variability in the dataset ensures that models trained on it can generalize well to real-world scenarios, making them robust and reliable. The dataset includes common fruits such as apples, bananas, oranges, and strawberries, among others, allowing for comprehensive training and evaluation.

Industry Use Cases One of the significant advantages of using the Fruits Dataset for Classification is its applicability in various fields such as agriculture, retail, and the food industry. In agriculture, it can help automate the process of fruit sorting and grading, enhancing efficiency and reducing labor costs. In retail, it can be used to develop automated checkout systems that accurately identify fruits, streamlining the purchasing process.

Educational Value The dataset is also valuable for educational purposes, providing students and educators with a practical tool to learn and teach machine learning concepts. By working with this dataset, learners can gain hands-on experience in data preprocessing, model training, and evaluation.

Conclusion The Fruits Dataset for Classification is a versatile and indispensable resource for advancing the field of image classification. Its diverse and high-quality images, coupled with practical applications, make it a go-to dataset for researchers, developers, and educators aiming to improve and innovate in machine learning and computer vision.

This dataset is sourced from Kaggle.

Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits

Versions and Updates:

The dataset's final version is the one presented here. No expectations for further development of the dataset at this moment.

If there seem to be any problems with the dataset, its descriptions, its properties or anything at all please contact me and I will help in what ways I can.

15/12/2021 - SYNASC2021 Paper

Based on the Bachelor's Thesis, a paper has been built and published at the SYNASC 2021 conference.

25/05/2021 - Bachelor's Thesis

The thesis, that had at its core building this fruit dataset and training CNN models on it, is finished and has been added. Information about the tasks and phases through which the dataset has been can also be found within the thesis.

25/05/2021 - Resized Dataset and Useful Scripts.

The resized dataset has been uploaded on 5 different dimensions along with some scripts that help in organizing and altering the dataset. The deployment took around 3-4 hours since some errors kept appearing when uploading. Sorry for any inconvenience.

Content

The following fruit types/labels/clades are included: abiu, acai, acerola, ackee, alligator apple, ambarella, apple, apricot, araza, avocado, bael, banana, barbadine, barberry, bayberry, beach plum, bearberry, bell pepper, betel nut, bignay, bilimbi, bitter gourd, black berry, black cherry, black currant, black mullberry, black sapote, blueberry, bolwarra, bottle gourd, brazil nut, bread fruit, buddha s hand, buffaloberry, burdekin plum, burmese grape, caimito, camu camu, canistel, cantaloupe, cape gooseberry, carambola, cardon, cashew, cedar bay cherry, cempedak, ceylon gooseberry, che, chenet, cherimoya, cherry, chico, chokeberry, clementine, cloudberry, cluster fig, cocoa bean, coconut, coffee, common buckthorn, corn kernel, cornelian cherry, crab apple, cranberry, crowberry, cupuacu, custard apple, damson, date, desert fig, desert lime, dewberry, dragonfruit, durian, eggplant, elderberry, elephant apple, emblic, entawak, etrog, feijoa, fibrous satinash, fig, finger lime, galia melon, gandaria, genipap, goji, gooseberry, goumi, grape, grapefruit, greengage, grenadilla, guanabana, guarana, guava, guavaberry, hackberry, hard kiwi, hawthorn, hog plum, honeyberry, honeysuckle, horned melon, illawarra plum, indian almond, indian strawberry, ita palm, jaboticaba, jackfruit, jalapeno, jamaica cherry, jambul, japanese raisin, jasmine, jatoba, jocote, jostaberry, jujube, juniper berry, kaffir lime, kahikatea, kakadu plum, keppel, kiwi, kumquat, kundong, kutjera, lablab, langsat, lapsi, lemon, lemon aspen, leucaena, lillipilli, lime, lingonberry, loganberry, longan, loquat, lucuma, lulo, lychee, mabolo, macadamia, malay apple, mamey apple, mandarine, mango, mangosteen, manila tamarind, marang, mayhaw, maypop, medlar, melinjo, melon pear, midyim, miracle fruit, mock strawberry, monkfruit, monstera deliciosa, morinda, mountain papaya, mountain soursop, mundu, muskmelon, myrtle, nance, nannyberry, naranjilla, native cherry, native gooseberry, nectarine, neem, nungu, nutmeg, oil palm, old world sycomore, olive, orange, oregon grape, otaheite apple, papaya, passion fruit, pawpaw, pea, peanut, pear, pequi, persimmon, pigeon plum, pigface, pili nut, pineapple, pineberry, pitomba, plumcot, podocarpus, pomegranate, pomelo, prikly pear, pulasan, pumpkin, pupunha, purple apple berry, quandong, quince, rambutan, rangpur, raspberry, red mulberry, redcurrant, riberry, ridged gourd, rimu, rose hip, rose myrtle, rose-leaf bramble, saguaro, salak, salal, salmonberry, sandpaper fig, santol, sapodilla, saskatoon, sea buckthorn, sea grape, snowberry, soncoya, strawberry, strawberry guava, sugar apple, surinam cherry, sycamore fig, tamarillo, tangelo, tanjong, taxus baccata, tayberry, texas persimmon, thimbleberry, tomato, toyon, ugli fruit, vanilla, velvet tamarind, watermelon, wax gourd, white aspen, white currant, white mulberry, white sapote, wineberry, wongi, yali pear, yellow plum, yuzu, zigzag vine, zucchini

Dataset properties

Total number of images: 225,640.

Number of classes: 262 fruits.

Number of images per label: Average: 861, Median: 1007, StDev: 276. (Initial target was 1,000 per label)

Image Width: Average: 213, Median: 209, StDev: 19.

Image Height: Average: 262, Median: 255, StDev: 30.

Missing Images from the initial 1,000 target: Average: 580, Median: 567, StDev: 258.

Format: a directory name represents a label and in each directory all the image data under the said label (the images are numbered but there might be missing numbers. The "renumber.py" script, if run, will fix the number gap problem).

Different varieties of the same fruit are generally stored in the same directory (Example: green, yellow and red apple).

The fruit images present in the dataset can contain the fruit in all the stages o...

This is a detailed description of the dataset, a data sheet for the dataset as proposed by Gebru et al.

Motivation for Dataset Creation Why was the dataset created? Embrapa ADD 256 (Apples by Drones Detection Dataset — 256 × 256) was created to provide images and annotation for research on *apple detection in orchards for UAV-based monitoring in apple production.

What (other) tasks could the dataset be used for? Apple detection in low-resolution scenarios, similar to the aerial images employed here.

Who funded the creation of the dataset? The building of the ADD256 dataset was supported by the Embrapa SEG Project 01.14.09.001.05.04, Image-based metrology for Precision Agriculture and Phenotyping, and FAPESP under grant (2017/19282-7).

Dataset Composition What are the instances? Each instance consists of an RGB image and an annotation describing apples locations as circular markers (i.e., presenting center and radius).

How many instances of each type are there? The dataset consists of 1,139 images containing 2,471 apples.

What data does each instance consist of? Each instance contains an 8-bits RGB image. Its corresponding annotation is found in the JSON files: each apple marker is composed by its center (cx, cy) and its radius (in pixels), as seen below:

"gebler-003-06.jpg": [ { "cx": 116, "cy": 117, "r": 10 }, { "cx": 134, "cy": 113, "r": 10 }, { "cx": 221, "cy": 95, "r": 11 }, { "cx": 206, "cy": 61, "r": 11 }, { "cx": 92, "cy": 1, "r": 10 } ],

Dataset.ipynb is a Jupyter Notebook presenting a code example for reading the data as a PyTorch's Dataset (it should be straightforward to adapt the code for other frameworks as Keras/TensorFlow, fastai/PyTorch, Scikit-learn, etc.)

Is everything included or does the data rely on external resources? Everything is included in the dataset.

Are there recommended data splits or evaluation measures? The dataset comes with specified train/test splits. The splits are found in lists stored as JSON files.

| | Number of images | Number of annotated apples | | --- | --- | --- | |Training | 1,025 | 2,204 | |Test | 114 | 267 | |Total | 1,139 | 2,471 |

Dataset recommended split.

Standard measures from the information retrieval and computer vision literature should be employed: precision and recall, F1-score and average precision as seen in COCO and Pascal VOC.

What experiments were initially run on this dataset? The first experiments run on this dataset are described in A methodology for detection and location of fruits in apples orchards from aerial images by Santos & Gebler (2021).

Data Collection Process How was the data collected? The data employed in the development of the methodology came from two plots located at the Embrapa’s Temperate Climate Fruit Growing Experimental Station at Vacaria-RS (28°30’58.2”S, 50°52’52.2”W). Plants of the varieties Fuji and Gala are present in the dataset, in equal proportions. The images were taken during December 13, 2018, by an UAV (DJI Phantom 4 Pro) that flew over the rows of the field at a height of 12 m. The images mix nadir and non-nadir views, allowing a more extensive view of the canopies. A subset from the images was random selected and 256 × 256 pixels patches were extracted.

Who was involved in the data collection process? T. T. Santos and L. Gebler captured the images in field. T. T. Santos performed the annotation.

How was the data associated with each instance acquired? The circular markers were annotated using the VGG Image Annotator (VIA).

WARNING: Find non-ripe apples in low-resolution images of orchards is a challenging task even for humans. ADD256 was annotated by a single annotator. So, users of this dataset should consider it a noisy dataset.

Data Preprocessing What preprocessing/cleaning was done? No preprocessing was applied.

Dataset Distribution How is the dataset distributed? The dataset is available at GitHub.

When will the dataset be released/first distributed? The dataset was released in October 2021.

What license (if any) is it distributed under? The data is released under Creative Commons BY-NC 4.0 (Attribution-NonCommercial 4.0 International license). There is a request to cite the corresponding paper if the dataset is used. For commercial use, contact Embrapa Agricultural Informatics business office.

Are there any fees or access/export restrictions? There are no fees or restrictions. For commercial use, contact Embrapa Agricultural Informatics business office.

Dataset Maintenance Who is supporting/hosting/maintaining the dataset? The dataset is hosted at Embrapa Agricultural Informatics and all comments or requests can be sent to Thiago T. Santos (maintainer).

Will the dataset be updated? There is no scheduled updates.

If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? Contributors should contact the maintainer by e-mail.

No warranty The maintainers and their institutions are exempt from any liability, judicial or extrajudicial, for any losses or damages arising from the use of the data contained in the image database.

We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count. The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit. We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mangoes. Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.

Fruits Nutritionix is a comprehensive image dataset containing 44 different classes of fruits, with each class representing a unique type of fruit. The dataset includes multiple images per class, capturing the fruits from various angles and in different lighting conditions to provide diverse visual representations.

Key Features: 1. 44 Classes: The dataset covers a wide variety of fruits, from common types like apples, oranges, and bananas to more exotic varieties. 2. High-Quality Images: Each class contains high-resolution images that display the fruits from different perspectives, ensuring detailed visual information. 3. Diverse Image Formats: Images in this dataset feature different backgrounds, lighting conditions, and orientations to simulate real-world scenarios, making it suitable for robust image classification tasks. 4. Multi-Purpose: This dataset is ideal for applications such as: - Image classification and recognition - Computer vision projects related to food and nutrition - Research in agricultural technology or food supply chain management

Use Cases: This dataset is particularly useful for training machine learning models to classify and recognize different types of fruits. It can also serve as a foundation for nutrition analysis or applications related to diet planning, fruit identification, and agricultural research.

This is an identification key to genera for seeds and fruits of the legume family. The coverage is world wide, and for each genus there are descriptions of the seeds and fruits, distribution data, and images. The interactive software system INTKEY is used for accessing the data and images. The key can be used for identifying to genus unknown legume samples or for querying the data and images for legume genera, and is designed for seed analysts, technicians, port inspectors, weed scientists, ecologists, botanists, and researchers who need to identify isolated legume fruits and seeds. Procedures relating to preparation, collection, and authentication of data are provided in the 'Procedures' resource file. In order to utilize the identification key the entire folder needs to be downloaded and extracted with all internal structure unmodified. Resources in this dataset:Resource Title: Legume (Fabaceae) Fruits and Seeds Version 2. File Name: Fabaceae.zipResource Description: This electronic database contains the following: 685 accepted legume genera with accepted scientific name and author(s) for each genus. No synonyms are given; for synonyms, refer to Polhill and Raven (1981), Gunn et al. (1992), and Mabberley (1997). The classification follows Polhill (1994a, 1994b), and has been modified according to recently published findings. The following is recorded for each genus: phylogenetic number, according to Polhill (1994b); subfamily; tribe; subtribe, when used; group, when used; number of species; and number of species examined to collect data for this database. 157 fruit characters and 128 seed characters for each genus. Unless indicated, these are original observations. 205 character and 1,379 generic images. When adequate materials were available, fruit and seed photographs and/or drawings, testa SEMs at 50 and 1,000 magnifications, and embryo and cotyledon drawings are given. The character images, whenever possible, were prepared from the generic images. For some characters, schematic drawings are presented. Native distribution of each genus Pertinent notes for each genus and tribe concerning their classification and fruits and seeds. Complete bibliography. Resource Title: Accepted legume genera information files. File Name: info.zipResource Description: 685 accepted legume genera with accepted scientific name and author(s) for each genus. No synonyms are given; for synonyms, refer to Polhill and Raven (1981), Gunn et al. (1992), and Mabberley (1997). This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource Text documents contain scientific name, subfamily, phylogenetic number, tribe, species in genus / species studied, fruit description, seed description, distribution, generic notes, and tribal notes. Resource Title: Character and generic images. File Name: images.zipResource Description: 205 character and 1,379 generic images. When adequate materials were available, fruit and seed photographs and/or drawings, testa SEMs at 50 and 1,000 magnifications, and embryo and cotyledon drawings are given. The character images, whenever possible, were prepared from the generic images. For some characters, schematic drawings are presented. This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource GIF images Resource Title: Procedures - Legume (Fabaceae) Fruits and Seeds v2. File Name: procs.rtfResource Description: Procedures relating to preparation, collection, and authentication of data This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resourceResource Title: Version History - Legume (Fabaceae) Fruits and Seeds. File Name: verhist.rtfResource Description: This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource

We developed the Vegetable Image Dataset to offer a collection of high-quality images featuring some of the most widely consumed and traded vegetables around the Pune. The dataset includes six types of vegetables: potato, chili, tomato, cucumber, beans, and okra. Each vegetable is further categorized into subclasses — potatoes are divided into three size-based classes (large, medium, and small) , while the other vegetables have two distinct varieties each (e.g., Chilies: Sitara, Jipoor, and Jwala; Tomatoes: Regular and Gaavran; Cucumbers: Regular and Gaavran; Beans: Long and Short; Okra: Long and Short) . This results in a total of 13 unique classes within the dataset . The images were taken under various lighting conditions — both natural and artificial — and against White backgrounds, including white, to ensure diversity and realism in the visual context. Given that the visual appearance of vegetables plays a significant role in their market value, this dataset supports research that evaluates vegetable quality through visual inspection . Although there are numerous datasets available for fruits and vegetables, many machine learning projects and applications still require a vegetable-specific dataset due to the unique nutritional importance and visual characteristics of vegetables . A robust dataset like this one enables machine learning models to achieve high accuracy in tasks such as classification and recognition . It's particularly useful for applications in research, education, and agriculture, including areas like detecting pest damage or monitoring quality degradation .

Fruit flies (Diptera: Tephritidae) are one of the most economically important groups of insects in the Afrotropical Region. They cause millions of Euros of damage to fruits and vegetables, and are a major constraint to commercial and subsistence farming in the region. The family Tephritidae includes more than 5000 species worldwide, approximately 1400 species of which develop in fleshy fruits (Norrbom et al 1999). Nearly 250 of these species are capable of achieving pest status by feeding on plants of economic importance (White and Elson-Harris 1992). The Mediterranean fruit fly, or Medfly, Ceratitis capitata (Wiedemann), is currently the most important of these pests from an invasive species perspective. Of African origin, it has spread to several other continents where it causes millions of Euros in damage. It also threatens other horticultural areas (such as California and Florida in the USA and regions in eastern Australia) resulting in very expensive detecting and monitoring programmes in these regions. The destructive association of several species with commercially grown fruit and vegetable crops makes them the subject of intensive agricultural research. But fruit flies are also biologically diverse and form a significant part of the biota of any region. Besides the several pest species, the large majority of the true fruit flies are limited to a small number of indigenous host fruits, mainly from trees and shrubs. Most of them are associated with forested areas, and can be used as indicator species for the biodiversity of a given area. In addition, several fruit fly larvae develop in other parts of host plants such as the stems or flowerheads. Fruit flies database is part of the ENBI WP13 feasibility study (Collaborative project between the Royal Belgian Institute of Natural Sciences, the Royal Museum for Central Africa and the National Botanical Garden. The data portal for the ENBI WP13 study can be found at http://projects.bebif.be/enbi

The dataset lists traditional species of fruit (256), vegetable (323) and pulse (50). The goal is gathering knowledge that could impulse projects that will contribute towards mainstreaming species with under-exploited potential in filling existing nutritional, income, and ecosystem gaps, and transforming African food systems. Existing knowledge on traditional foods is highly fragmented, scattered in various publications and reports, or in inaccessible databases to policy makers and practitioners. Based on accepted names published by the World Flora Online (WFO), the dataset lists traditional fruits, vegetables, and pulses species in Senegal, Ivory Coast, Benin, Ethiopia, and Kenya. For all species, data is indicated for plant type, production type, food group, edible parts, and how consumed. Extra information including other uses, key nutrients, and toxicity is also indicated for some species.

OCID – Object Clutter Indoor Dataset

Developing robot perception systems for handling objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms.

The Object Cluttered Indoor Dataset is an RGBD-dataset containing point-wise labeled point-clouds for each object. The data was captured using two ASUS-PRO Xtion cameras that are positioned at different heights. It captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter. In addition OCID does also provide ground-truth data for other vision tasks like object-classification and recognition.

OCID comprises 96 fully built up cluttered scenes. Each scene is a sequence of labeled pointclouds which are created by building a increasing cluttered scene incrementally and adding one object after the other. The first item in a sequence contains no objects, the second one object, up to the final count of added objects.

Dataset

The dataset uses 89 different objects that are chosen representatives from the Autonomous Robot Indoor Dataset(ARID)[1] classes and YCB Object and Model Set (YCB)[2] dataset objects.

The ARID20 subset contains scenes including up to 20 objects from ARID. The ARID10 and YCB10 subsets include cluttered scenes with up to 10 objects from ARID and the YCB objects respectively. The scenes in each subset are composed of objects from only one set at a time to maintain separation between datasets. Scene variation includes different floor (plastic, wood, carpet) and table textures (wood, orange striped sheet, green patterned sheet). The complete set of data provides 2346 labeled point-clouds.

OCID subsets are structured so that specific real-world factors can be individually assessed.

ARID20-structure

• location: floor, table
• view: bottom, top
• scene: sequence-id
• free: clearly separated (objects 1-9 in corresponding sequence)
• touching: physically touching (objects 10-16 in corresponding sequence)
• stacked: on top of each other (objects 17-20 in corresponding sequence)

ARID10-structure

• location: floor, table
• view: bottom, top
• box: objects with sharp edges (e.g. cereal-boxes)
• curved: objects with smooth curved surfaces (e.g. ball)
• mixed: objects from both the box and curved
• fruits: fruit and vegetables
• non-fruits: mixed objects without fruits
• scene: sequence-id

YCB10-structure

• location: floor, table
• view: bottom, top
• box: objects with sharp edges (e.g. cereal-boxes)
• curved: objects with smooth curved surfaces (e.g. ball)
• mixed: objects from both the box and curved
• scene: sequence-id

Structure:

You can find all labeled pointclouds of the ARID20 dataset for the first sequence on a table recorded with the lower mounted camera in this directory:

./ARID20/table/bottom/seq01/pcd/

In addition to labeled organized point-cloud files, corresponding depth, RGB and 2d-label-masks are available:

• pcd: 640×480 organized XYZRGBL-pointcloud file with ground truth
• rgb: 640×480 RGB png-image
• depth: 640×480 16-bit png-image with depth in mm
• label: 640×480 16-bit png-image with unique integer-label for each object at each pixel

Dataset creation using EasyLabel:

OCID was created using EasyLabel – a semi-automatic annotation tool for RGBD-data. EasyLabel processes recorded sequences of organized point-cloud files and exploits incrementally built up scenes, where in each take one additional object is placed. The recorded point-cloud data is then accumulated and the depth difference between two consecutive recordings are used to label new objects. The code is available here.

OCID data for instance recognition/classification

For ARID10 and ARID20 there is additional data available usable for object recognition and classification tasks. It contains semantically annotated RGB and depth image crops extracted from the OCID dataset.

The structure is as follows:

• type: depth, RGB
• class name: eg. banana, kleenex, …
• class instance: eg. banana_1, banana_2, kleenex_1, kleenex_2,…

The data is provided by Mohammad Reza Loghmani.

Research paper

If you found our dataset useful, please cite the following paper:

@inproceedings{DBLP:conf/icra/SuchiPFV19,

author = {Markus Suchi and

Timothy Patten and

David Fischinger and

Markus Vincze},

title = {EasyLabel: {A} Semi-Automatic Pixel-wise Object Annotation Tool for

Creating Robotic {RGB-D} Datasets},

booktitle = {International Conference on Robotics and Automation, {ICRA} 2019,

Montreal, QC, Canada, May 20-24, 2019},

pages = {6678--6684},

year = {2019},

crossref = {DBLP:conf/icra/2019},

url = {https://doi.org/10.1109/ICRA.2019.8793917},

doi = {10.1109/ICRA.2019.8793917},

timestamp = {Tue, 13 Aug 2019 20:25:20 +0200},

biburl = {https://dblp.org/rec/bib/conf/icra/SuchiPFV19},

bibsource = {dblp computer science bibliography, https://dblp.org}

}

@proceedings{DBLP:conf/icra/2019,

title = {International Conference on Robotics and Automation, {ICRA} 2019,

Montreal, QC, Canada, May 20-24, 2019},

publisher = {{IEEE}},

year = {2019},

url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8780387},

isbn = {978-1-5386-6027-0},

timestamp = {Tue, 13 Aug 2019 20:23:21 +0200},

biburl = {https://dblp.org/rec/bib/conf/icra/2019},

bibsource = {dblp computer science bibliography, https://dblp.org}

}

Contact & credits

For any questions or issues with the OCID-dataset, feel free to contact the author:

• Markus Suchi – email: suchi@acin.tuwien.ac.at
• Tim Patten – email: patten@acin.tuwien.ac.at

For specific questions about the OCID-semantic crops data please contact:

• Mohammad Reza Loghmani – email: loghmani@acin.tuwien.ac.at

References

[1] Loghmani, Mohammad Reza et al. "Recognizing Objects in-the-Wild: Where do we Stand?" 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018): 2170-2177.

[2] Berk Calli, Arjun Singh, James Bruce, Aaron Walsman, Kurt Konolige, Siddhartha Srinivasa, Pieter Abbeel, Aaron M Dollar, Yale-CMU-Berkeley dataset for robotic manipulation research, The International Journal of Robotics Research, vol. 36, Issue 3, pp. 261 – 268, April 2017.

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

This is USDA's Agricultural Marketing Service's list of wholesale markets, or facilities where wholesalers receive large quantities of commodities by rail, truck, and air from local growers as well as producers around the world for sale to grocers, restaurants, institutions, and other businesses. About 90% of wholesale markets sell fresh fruits and vegetables, but there are also seafood, meat, and flower wholesale markets.

In prospective human exploration of outer space the need to maintain a species over several generations under changed gravity conditions may arise. This paper reports the analysis of the third generation of fruit fly Drosophila melanogaster obtained during the 44.5-day space flight (Foton-M4 satellite 2014 Russia) followed by the fourth generation on Earth and the fifth generation under conditions of a 12-day space flight (2014 in the Russian Segment of the ISS). The obtained results show that it is possible to obtain the third-fifth generations of a complex multicellular Earth organism under changed gravity conditions (in the cycle weightlessness - Earth - weightlessness) which preserves fertility and normal development. However there were a number of changes in the expression levels and content of cytoskeletal proteins that are the key components of the spindle apparatus and the contractile ring of cells.

Plant traits are critical to plant form and function —including growth, survival and reproduction— and therefore shape fundamental aspects of population and ecosystem dynamics as well as ecosystem services. Here, we present a global species-level compilation of key functional traits for palms (Arecaceae), a plant family with keystone importance in tropical and subtropical ecosystems. We derived measurements of essential functional traits for all (>2500) palm species from key sources such as monographs, books, other scientific publications, as well as herbarium collections. This includes traits related to growth form, stems, armature, leaves and fruits. Although many species are still lacking trait information, the standardized and global coverage of the data set will be important for supporting future studies in tropical ecology, rainforest evolution, paleoecology, biogeography, macroecology, macroevolution, global change biology and conservation. Potential uses are comparative eco-evolutionary studies, ecological research on community dynamics, plant-animal interactions and ecosystem functioning, studies on plant-based ecosystem services, as well as conservation science concerned with the loss and restoration of functional diversity in a changing world.

This web layer contains data of state level fruit production statistics and contains information about fruit cultivation area.India's diverse climate ensures availability of all varieties of fresh fruits & vegetables. It ranks second in fruits and vegetables production in the world, after China. As per National Horticulture Database (Second Advance Estimates) published by National Horticulture Board, during 2019-20, India produced 99.07 million metric tons of fruits and 191.77 million metric tons of vegetables. The area under cultivation of fruits stood at 6.66 million hectares while vegetables were cultivated at 10.35 million hectares.According to FAO (2019), India is the largest producer of ginger and okra amongst vegetables and ranks second in production of potatoes, onions, cauliflowers, brinjal, Cabbages, etc. Amongst fruits, the country ranks first in production of Bananas (26.08%), Papayas (44.05%) and Mangoes (including mangosteens and guavas) (45.89%).The vast production base offers India tremendous opportunities for export. During 2020-21, India exported fruits and vegetables worth Rs. 9,940.95 crores/ 1,342.14 USD Millions which comprised of fruits worth Rs. 4,971.22 crores/ 674.53 USD Millions and vegetables worth Rs. 4,969.73 crores/ 667.61 USD Millions.Grapes, Pomegranates, Mangoes, Bananas, Orange’s account for larger portion of fruits exported from the country while Onions, Mixed Vegetables, Potatoes, Tomatoes, and Green Chilly contribute largely to the vegetable export basket.The major destinations for Indian fruits and vegetables are Bangladesh, UAE, Netherland, Nepal, Malaysia, UK, Sri Lanka, Oman and Qatar.The attributes are given below for this web map:ItemsFruit Cultivation Area (Ha)Production (MT)This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

DescriptionThis dataset contains processed data for flower retention in response to flower position, presence of basal fruits, and nursery pollinator oviposition from a field experiment using Yucca glauca. Refer to manuscript for details of the study system, study site, and methods. FormatThe file is in .csv format. Each row contains data for an inflorescence.Description of column headersinfl - ID of the inflorescence protected for use in the experiment. All IDs are numeric IDs, except for one that is alphanumeric.x - Universal Transverse Mercator (UTM, Zone 14T, datum WGS 84) Easting coordinate of inflorescencey - Universal Transverse Mercator (UTM, Zone 14T, datum WGS 84) Northing coordinate of inflorescenceelev - Elevation above sea level (m) of inflorescenceht_cm - Height (cm) of the inflorescence from the point of origin at the base of the rosette to the tip.basaldia_mm - Basal diameter (mm) of the inflorescence's rosette (an index of rosette size) measured below the lowest crown of green leavesTrose - Total number of rosettes in the visibly-identifiable Yucca glauca clump with the focal inflorescenceOinfl - Number of other inflorescences in the visibly-identifiable Yucca glauca clump. If inflorescences existed they were broken before the experiment began.buds - Total number of buds/flowers on the inflorescencebeetles - Number of Carpohilus sp. beetles counted on and removed from the focal inflorescence at the time of protecting the inflorescence with a mesh sleeve.aphids - Presence of aphids on the inflorescence at any time during the experiment (binary variable).context - The inflorescence treatment assigned to the inflorescence. F refers to basal fruits B refers to buds. One and 0 refer to presence or absence of fruits or buds. There were three inflorescence treatments, (1) 3 basal experimental flowers with buds above (F0B1), (2) 3 distal experimental flowers with no basal fruits and no buds above (F0B0), and (3) 3 distal experimental flowers with 1 to 3 basal fruits and no buds above (F1B0).ovi - The artificial oviposition treatment assigned to the inflorescence. There were three treatments, (1) no oviposition treatment where no wounds were applied (Z), (2) low oviposition treatment where 6 wounds were applied (L), and (3) high oviposition treatment where 24 wounds were applied (H).Fr - Number of basal fruits on the inflorescence as part of the assigned treatment. Inflorescence treatments F0B1 and F0B0 (see context) had zero basal fruits. The number of basal fruits for inflorescence treatment F1B0 (see context) were recorded.B - Number of buds on the inflorescence as part of the assigned treatment. There were no buds for inflorescence treatments F1B0 and F0B0 (see context). The total number of buds above basal flowers in inflorescence treatment F0B1 (see context) were recorded. The number of buds for one of the inflorescence is missing (NA).donor - ID of the donor plant that was used to hand-pollinate experimental flowers. Some of these included inflorescences used in the experiment and have the same ID as in infl. Contains both numeric and alphanumeric IDs.MD - Date on which the inflorescence was manipulated to obtain the required inflorescence treatment. Manipulations involved removal of flowers and buds, hand-pollination of basal flowers for the F1B0 treatment (see context). Dates are in mm/dd/yyyy format.TD - Date on which the artificial oviposition treatment was applied to the experimental flowers and they were hand-pollinated. This was usually later then manipulation date (MD) for the F1B0 treatment (see context) because time was needed basal fruits to be initiated before the oviposition treatment could be applied. Dates are in mm/dd/yyyy format.CD - Date on which the inflorescence was checked to determine how many experimental flowers (out of 3 total flowers) were retained i.e. developed into fruits. This was 10 days after the treatment date (TD). Dates are in mm/dd/yyyy format.fruitsR - The number of experimental flowers that were retained (out of 3 experimental flowers)FD - Date on which fruits were collected for weighing. This was 25 days after the treatment date (TD).E1W - The mass of the the first fruit from experimental flowers (g), if any. If no fruits existed, or if the fruit was lost, NA was used.E2W - The mass of the second fruit from experimental flowers (g), if any. If no second fruit existed, or if the fruit was lost, NA was used.E3W - The mass of the third fruit from experimental flowers (g), if any. If no third fruit existed, or if the third fruit was lost, NA was used.avgRFwt - The average mass of fruits from experimental flowers (average of E1W, E2W, and E3W). If only one fruit mass was present, that value was used.

392 Global import shipment records of Dried Fruits with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Motivation

Waste. As we all know that waste has become commonplace in many countries in the world. There is nothing wrong, in terms of waste itself defined as the final product that can no longer be used (by humans); residue. The problem lies in 'how do we manage this waste, while we can't use it anymore?'. Several countries have issues related to waste because the rate of waste production is not comparable to its management efforts. These things can be a big problem for the ecosystem.

With this dataset. I hope we can help waste management efforts with computer vision technology. With this technology, we can identify, track, sort and process it accordingly.

Content

This dataset contains approximately 256K images (156K original data) representing two classes, Biodegradable and Non-biodegradable. - Biodegradable, contains materials which can be decomposed naturally by microorganisms, such as foods, plants, fruits, etc. The waste from this material can be processed into compost. - Non-biodegradable, contains materials that cannot be decomposed naturally, for example plastics, metals, inorganic elements, etc. The waste from this material will be recycled into new materials.

I add augmented data against imbalanced class. Augmented data made by manipulating original data. Image transformation used: horizontal flip, vertical flip, 90deg CW rotation, 90deg CCW rotation.

In this dataset, I divide the data into two subsets, training set and evaluation set. The training set itself was splitted into 4 parts due to some technical constraints (my internet bandwidth). The thing to know is that the part of the training set don't have a good data distribution. So, don't pass each part directly to your model. Concatenate each part to single dataset. See Quickstart.

Data files in this dataset have unique name to prevent them from overwritten theirself when concatenating. Below is filename reference. You will need this for filtering this dataset.

SUBSET.PART_CLASS_CATEGORY_ID.EXT

SUBSET, the subset where data belong in. Either TEST or TRAIN. PART, part number of subset. Only if the subset splitted into several parts. CLASS, the class of data. BIODEG for biodegradable, or NBIODEG for non-biodegradable. CATEGORY, category of data. ORI for original data, HFL for horizontal flip, VFL for vertical flip, CWR for clockwise rotation, CCW for counter clockwise rotation. ID, data identification number. EXT, data extension. Either .jpg or .jpeg.

Acknowledgements

In this part, i would like to give an attribution to several Kaggle's users because without their great work, this dataset would be incomplete. As i mentioned that this dataset's source consist of another Kaggle dataset. So, this is my responsibility to do this. - Food Images (Food-101) - (K Scott Mader) - Fruit and Vegetable Image Recognition - (Kritik Seth) - Waste Classification data - (Sashaank Sekar) - Waste Classification Data v2 - (sapal6) - waste_pictures - (且听风吟)

As per world agriculture statistics India is the world's largest producer of many fresh fruits like banana, mango, guava, papaya, lemon and vegetables like chickpea, okra and milk, major spices like chili pepper, ginger, fibrous crops such as jute, staples such as millets and castor oil seed. India is the second largest producer of wheat and rice, the world's major food staples.

India is currently the world's second largest producer of several dry fruits, agriculture-based textile raw materials, roots and tuber crops, pulses, farmed fish, eggs, coconut, sugarcane and numerous vegetables. India is ranked under the world's five largest producers of over 80% of agricultural produce items, including many cash crops such as coffee and cotton, in 2010. India is one of the world's five largest producers of livestock and poultry meat, with one of the fastest growth rates, as of 2011.

One report from 2008 claimed that India's population is growing faster than its ability to produce rice and wheat.[20] While other recent studies claim that India can easily feed its growing population, plus produce wheat and rice for global exports, if it can reduce food staple spoilage/wastage, improve its infrastructure and raise its farm productivity like those achieved by other developing countries such as Brazil and China.

Data collected from Ministry of Agriculture and Farmers Welfare of India

902 Global import shipment records of Cherry Fruit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.