Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

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.

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

This dataset is about books. It has 1 row and is filtered where the book is The Apple revolution : Steve Jobs, the counterculture and how the crazy ones took over the world. It features 7 columns including author, publication date, language, and book publisher.

Context

This dataset is collected during project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab". The collection of digital images were carried out in different locations of Latvia. Digital images were collected by the Institute of Horticulture (LatHort).

Content

Dataset contains classified images saved in two folders: "Healthy" and "Scab" to identify their categories. There is another dataset with scab symptoms on apple leaves, which is available on Kaggle too.

Attribution

If you use our dataset, please cite as: S. Kodors, G. Lacis, O. Sokolova, V. Zhukovs, I. Apeinans and T. Bartulsons. 2021. Apple Scab Detection using CNN and Transfer Learning. Agronomy Research, 19(2), 507–519. doi: 10.15159/AR.21.045

Inspiration

This dataset is distributed under license (CC BY-NC-ND 4.0). https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" alt="Creative Commons License">

This database comprises occurrence data, including both specimen and observation data, obtained from the whole distribution range (native and invaded) for the freshwater snail Pomacea canaliculata (Caenogastropoda: Ampullariidae). This species is native from lower Del Plata basin in South America but, together with other congeners collectively known as "apple snails", were introduced to many regions outside their natural ranges where they rapidly spread out, causing serious damage to aquatic crops and also to biodiversity and functioning of natural wetlands. The aim of this data publication is to provide an open access, updated and accurate database of P. canaliculata records worldwide, available for use in ecological studies and pest management, focusing on discriminate misidentifications with other apple snails. This database includes 718 records of P. canaliculata from 29 countries distributed in Africa, South America, North America, Asia and Pacific Islands, and were reported from the early 20th century until present day. The records reported here were compiled from different sources: - Our personal records which include samples collected during the past 25 years covering a large area of many provinces in Argentina. - Available bibliography, searching for any reliable report mentioning geographic coordinates or at least a precise locality, excluding those with doubtful identity or not determined records such as “Pomacea” or “Pomacea sp.”. - By request to several researchers with expertise in this species around the world to provide us records and also their expert opinion to discard records corresponding to other congeners (especially the often-confounded P. maculata).

Sure! Here's a concise data description within 3000 characters for a project titled "Good and Bad Classification of Apples":

Project Title: Good and Bad Classification of Apples

Data Description:

The dataset used in this project is centered around the classification of apples into two categories: good (fit for sale/consumption) and bad (damaged, rotten, or otherwise unfit). The dataset comprises images of apples collected under controlled as well as natural conditions, and optionally, corresponding annotations or metadata.

1. Data Types:

Image Data: The primary data consists of RGB images of individual apples.

Labels: Each image is labeled as either “good” or “bad”.

Optional Metadata (if available):

Time of capture

Lighting condition

Apple variety

Temperature or humidity readings at the time of image capture

1. Image Characteristics:

Resolution: Images range from 224x224 to 512x512 pixels.

Background: Mixture of plain (controlled lab settings) and complex (orchard or market environments).

Lighting: Includes both natural and artificial lighting.

Angle and Orientation: Varies to simulate real-world usage scenarios in sorting systems.

1. Good Apples:

Visually appealing

No visible bruises, rot, or mold

Uniform shape and color

Examples might show apples with minimal surface blemishes or minor imperfections

1. Bad Apples:

Presence of:

Mold

Bruising

Cuts or cracks

Discoloration or rot

Some may be partially decomposed

Often irregular in shape or visibly damaged

1. Sources:

Agricultural research datasets

Custom image captures from farms or marketplaces

Open-source image repositories with suitable licensing (e.g., Creative Commons)

1. Data Split:

Training set: 70%

Validation set: 15%

Test set: 15%

Stratified to ensure balanced class representation across splits

1. Preprocessing:

Image resizing and normalization

Data augmentation (flipping, rotation, brightness/contrast adjustments) to increase model robustness

Optional noise filtering and background removal to improve focus on the apple surface

1. Use Cases:

Automated sorting systems in agriculture

Quality control for fruit suppliers and supermarkets

Educational tools for machine learning in agricultural contexts

Let me know if you’d like to include technical details about models or preprocessing pipelines as well.

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...