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FruitNet and FruitBox Dataset

25097459
Tagfood
Taskobject detection
Release YearMade in 2023
Licenseunknown

Introduction #

Misaj Sharafudeen, Vinod Chandra

The authors separate datasets were compiled: FruitNet and FruitBox Dataset. They proposed an interlaced deep neural framework that aids in the accurate qualitative (freshness—good/bad) and quantitative (weights in kilograms) predictive analysis of tropical fruits and their origin in wholesale and retail fruit boxes.

Motivation

Fruits have always played a vital role in maintaining a healthy diet, and preserving their quality is paramount for individuals’ well-being. The prevalence of consumerism has significantly impacted the production sector, leading to the packaging of fruits in containers and boxes by manufacturers. Accurately determining the precise weights of these fruit containers is essential to ensure transparency and fairness. Traditional techniques are time and cost-consuming and may even result in weight manipulation due to their inefficiency. Fruits can be graded according to their physical characteristics to help determine their freshness; however, this method requires a lot of manual labor. The issue of estimating the quantity and freshness of eatable produce only from images is complex, and no compelling study has been researched and developed.

The proposed solution employs an integrated deep neural architecture to facilitate precise identification of fruits within wholesale fruit boxes, followed by comprehensive qualitative and quantitative predictive analysis. Furthermore, the algorithm takes into account various fruit classes to determine the weight of the box and ascertain its origin. This adaptable technique holds promise for diverse wholesale sectors. Its primary aim is to supplant manual inspection methods entirely with more efficient and cost-effective alternatives, thus significantly reducing expenses associated with manual inspection. The development of fruit classification systems has been driven by the needs of industrial settings such as factories and hypermarkets, where accurate sorting of different fruit varieties is paramount. Recent progress, achieved by integrating public benchmark datasets with cutting-edge convolutional neural networks (CNNs), has led to the deployment of highly accurate fruit classification models. A comprehensive dataset was compiled by amalgamating high-quality fruit images captured physically with images scraped from public sources, with a focus on including non-curated images in automated systems. To tackle data scarcity, researchers have explored the use of generative models in addition to traditional data augmentation techniques. Initial studies focused on using shape and size features to detect and localize fruits on plants. Gradually, solutions shifted toward the utilization of deep neural architectures.

Dataset description

Separate datasets were curated to develop an accurate quality recognition system, estimate weight measures, and determine geographic origin. The annotated FruitNet dataset was compiled by localizing annotations on fruit classes in the FruitNet dataset. To streamline the process of fruit box detection, the authors enhanced the quality detector training to include box detections, thereby collecting and annotating images of containersand baskets of apples from market traders. The instances were manually labeled using the LabelImg tool within the Python environment. The initial dataset comprised images depicting various qualities of six different fruits, captured from diverse angles and backgrounds. This study focuses on three specific fruit categories that share similar shapes and possess an adequate number of instances. The objective is to discern and categorize the quality of individual fruits within a fruit box as either good or bad. Specifically, the authors chose images containing solitary instances of apples, oranges, and guavas. Moreover, meticulous annotation was applied to the apples within containers and fruit boxes to facilitate grading detection. The FruitBox dataset was meticulously compiled, featuring containerized apples along with their corresponding weights, obtained using a 12-megapixel camera from trading stores and markets. To enrich the dataset, images of fruit boxes were sourced through web scraping. Annotations generated from the object detection model assisted in determining the regions of interest for weight calculation. This comprehensive dataset comprises 2052 instances of fruit boxes, each accompanied by its measured weight in kilograms.

The Fruit360 dataset, containing 131 different fruit types, was utilized for the recognition of fruit variants and their respective origins. Specifically, eight variants of apples originating from six different countries were grouped together to estimate the origins of fruit boxes. These variants were then divided into training and testing sets to aid in the learning process. The model’s versatility allows for easy transferability to different fruit types, depending on the specific fruits contained within the boxes.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

FruitNet and FruitBox Dataset is a dataset for object detection and identification tasks. It is used in the food industry.

The dataset consists of 2509 images with 3989 labeled objects belonging to 7 different classes including good apple, bad guava, good guava, and other: bad apple, bad orange, fruit box, and good orange.

Images in the FruitNet and FruitBox Dataset dataset have bounding box annotations. There are 1076 (43% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: fruit box (1075 images), fruit net train (907 images), and fruit net val (527 images). Additionally, some images marked with its weight tag. The dataset was released in 2023 by the University of Kerala, India.

Dataset Poster

Explore #

FruitNet and FruitBox Dataset dataset has 2509 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
OpenSample annotation mask from FruitNet and FruitBox DatasetSample image from FruitNet and FruitBox Dataset
👀
Have a look at 2509 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 7 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-7 of 7
Class
Images
Objects
Count on image
average
Area on image
average
good apple
rectangle
438
2368
5.41
25.91%
bad guava
rectangle
303
303
1
38.25%
good guava
rectangle
281
734
2.61
27.66%
bad apple
rectangle
197
197
1
46.36%
bad orange
rectangle
189
189
1
27.23%
fruit box
rectangle
172
172
1
69.05%
good orange
rectangle
26
26
1
26.31%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-7 of 7
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
good apple
rectangle
2368
5.22%
52.1%
0%
1px
0.44%
502px
75.78%
89px
19.14%
1px
0.28%
508px
71.88%
good guava
rectangle
734
10.8%
58.34%
0%
1px
0.53%
383px
79.3%
105px
26.34%
1px
0.38%
399px
89.84%
bad guava
rectangle
303
38.25%
64.26%
16.49%
94px
36.72%
195px
76.17%
157px
61.34%
111px
43.36%
226px
88.28%
bad apple
rectangle
197
46.36%
74.51%
13.19%
94px
36.72%
226px
88.28%
159px
62.26%
91px
35.55%
229px
89.45%
bad orange
rectangle
189
27.23%
41.02%
20.49%
94px
36.72%
143px
55.86%
114px
44.47%
95px
49.48%
150px
78.12%
fruit box
rectangle
172
69.05%
100%
28.44%
103px
34.68%
1341px
100%
457px
83.12%
121px
44%
2489px
100%
good orange
rectangle
26
26.31%
36.18%
0.78%
20px
5%
156px
60.94%
132px
51.34%
42px
15.67%
152px
59.38%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 3989 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 3989
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
fruit box
rectangle
Image_121.jpg
178 x 284
144px
80.9%
258px
90.85%
73.49%
2
good apple
rectangle
Image_121.jpg
178 x 284
53px
29.78%
69px
24.3%
7.23%
3
good apple
rectangle
Image_121.jpg
178 x 284
51px
28.65%
75px
26.41%
7.57%
4
good apple
rectangle
Image_121.jpg
178 x 284
68px
38.2%
85px
29.93%
11.43%
5
good apple
rectangle
Image_121.jpg
178 x 284
65px
36.52%
74px
26.06%
9.51%
6
bad guava
rectangle
IMG_20190822_080331_1.jpg
256 x 256
166px
64.84%
186px
72.66%
47.11%
7
bad orange
rectangle
IMG_20190824_174852.jpg
256 x 192
117px
45.7%
116px
60.42%
27.61%
8
good apple
rectangle
20190809_161600.jpg
256 x 256
115px
44.92%
131px
51.17%
22.99%
9
bad orange
rectangle
IMG_20190824_174928.jpg
256 x 192
106px
41.41%
110px
57.29%
23.72%
10
fruit box
rectangle
Image_136.jpg
225 x 225
221px
98.22%
215px
95.56%
93.86%

License #

License is unknown for the FruitNet and FruitBox Dataset dataset.

Source

Citation #

If you make use of the FruitNet and FruitBox Dataset data, please cite the following reference:

@dataset{FruitNet and FruitBox Dataset,
  author={Misaj Sharafudeen and Vinod Chandra},
  title={FruitNet and FruitBox Dataset},
  year={2023},
  url={https://www.kaggle.com/datasets/mirlab/annotated-fruitnet-and-fruitbox}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-fruit-net-box-dataset,
  title = { Visualization Tools for FruitNet and FruitBox Dataset Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/fruit-net-box } },
  url = { https://datasetninja.com/fruit-net-box },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Please visit dataset homepage to download the data.

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