Indian Biological Images Archive

Project Accession:	IBIAP_1000000016
Title:	Dry fruit image dataset for machine learning applications
Representative Image:
Description:	The "Dry Fruit Image Dataset" is a collection of 11500+ processed high-quality images representing 12 distinct classes of dry fruits. The 4 dry fruits—Almonds, Cashew Nuts, Raisins, and Dried Figs (Anjeer)—along with 3 sub-types of each are contained in the sub-folders, making a total of 12 distinct classes. These pictures were taken with a high-definition camera on cell phones. The dataset contains images in different lighting conditions as well as with different backgrounds. This dataset can be used for building machine learning models for the classification and recognition of Dry Fruits, requiring neat, appropriately tagged, and high-quality images. The dry fruit classification algorithm can be trained, tested, and validated using this dataset. Furthermore, it is beneficial for dry fruit research, education, and medicinal purposes.
Publications:	https://doi.org/10.1016/j.dib.2023.109325
Funding agency:	N/A
Grant Number:	N/A
Ethics Statement:	Download
Any Other Information :	The original version of the dataset is available at Mendeley Data (https://data.mendeley.com/datasets/yfhgn8py5f/1). The Mendeley Data citation is: Choudhary, Chetan; Kale, Atharva ; Rajput, Jaideep; Meshram, Vishal; Meshram, Vidula (2023), “Dry Fruit Image Dataset”, Mendeley Data, V1, doi: 10.17632/yfhgn8py5f.1. Please refer to Table 3 of published article, for Artificial light specifications.
Additional File:	Download
Acknowledgments:	No specific grant was provided for this research by public, private, or not-for-profit funding organizations.

Sr.No	First name	Last name	Email	Organization	Designation
1	Vishal	Meshram	vishal.meshram@viit.ac.in	Vishwakarma Institute of Information Technology, Pune, India	Principal Investigator
2	Chetan	Choudhary	N/A	Vishwakarma Institute of Information Technology, Pune, India	Unspecified
3	Atharva	Kale	N/A	Vishwakarma Institute of Information Technology, Pune, India	Unspecified
4	Jaideep	Rajput	N/A	Vishwakarma Institute of Information Technology, Pune, India	Unspecified
5	Vidula	Meshram	N/A	Vishwakarma Institute of Information Technology, Pune, India	Unspecified
6	Amol	Dhumane	N/A	Pimpri Chinchwad College of Engineering, Pune, India	Unspecified

Study Accession:	PPS_1000000020
Title:	Dry fruit image dataset for machine learning applications
Imaging Type:	Plant Photography (PP)
Imaging Sub-type:	Not Applicable
Summary:	Dry fruits are convenient and nutritious snacks that can provide numerous health benefits. They are packed with vitamins, minerals, and fibres, which can help improve overall health, lower cholesterol levels, and reduce the risk of heart disease. Due to their health benefits, dry fruits are an essential part of a healthy diet. In addition to health advantage, dry fruits have high commercial worth. The value of the global dry fruit market is estimated to be USD 6.2 billion in 2021 and USD 7.7 billion by 2028. The appearance of dry fruits is utilized for assessing their quality to a great extent, requiring neat, appropriately tagged, and high-quality images. Hence, this dataset is a valuable resource for the classification and recognition of dry fruits. With over 11500+ high-quality processed images representing 12 distinct classes, this dataset is a comprehensive collection of different varieties of dry fruits. The four dry fruits included in this dataset are Almonds, Cashew Nuts, Raisins, and Dried Figs (Anjeer), along with three subtypes of each. This makes it a total of 12 distinct classes of dry fruits, each with its unique features, shape, and size. The dataset will be useful for building machine learning models that can classify and recognize different types of dry fruits under different conditions, and can also be beneficial for dry fruit research, education, and medicinal purposes. Due to their nutritional value and health advantages, dry fruits have been consumed for a very long time. One of the best strategies to improve general health is to include dry fruits in the diet.
Keywords:	Computer vision; Dehydrated fruits; Fruit Classification; Fruit detection; Image classification; Machine learning
Additional / Any Other Information:	N/A
Release Date:	April 24, 2025
Access Licence Type:	Open Access

Table 1. The sample types registered under this study are as follows:

Sample Type ID	Organism	Taxon ID	Biological Entity	Laterality	Source Tissue	Source Cell/Cell-line	Cell Organelle
PPSMT_10000000045	Prunus dulcis	3755	Seed	Not Applicable	N/A	N/A	N/A
PPSMT_10000000046	Anacardium occidentale	171929	Seed	Not Applicable	N/A	N/A	N/A
PPSMT_10000000047	Ficus carica	3494	Fruit	Not Applicable	N/A	N/A	N/A
PPSMT_10000000048	Vitis vinifera	29760	Fruit	Not Applicable	N/A	N/A	N/A

Table 2. The samples registered under this study are as follows:

Sample Type ID	Sample ID	Plant Part Used	Plant Variety Name	Sample Source	Data Collection Duration	Data Source Location	Dry Fruit Class	Dry Fruit Subclass	Geographic Location (region and locality)	Image Capture Direction	Image Data Type
PPSMT_10000000048	PPSM_10000258287	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258301	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258310	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258317	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258325	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258333	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258342	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258349	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258355	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258365	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258366	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258381	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258397	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258413	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258438	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258445	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258461	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258463	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258477	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit
PPSMT_10000000048	PPSM_10000258493	Fruit	Raisin_Premium	Vishwakarma Institute of Information Technology, Kapil Nagar, Kondhwa Budruk, Pune – 411048, Maharashtra, India.	February to March	N/A	Raisin	Premium	Kapil Nagar, Kondhwa Budruk, Pune	Front/Back/Side/Top/Bottom	Dry Fruit

Table 3. The experiment types registered under this study are as follows:

Experiment Type ID	Instrument Name	Instrument Type	Manufacturer	Model
PPET_10000000018	Camera	Mobile	Apple/Motorola	iPhone13/Moto G40 fusion

Experimental Design Summary (PPET_10000000018)
The dry fruit images were captured using two different makes of camera, that were Apple's iPhone 13 and Motorola's Moto G40 fusion mobiles' rear camera having high resolution. In all, 11500+ images were captured with a camera and then stored in various folders according to their category and classification. Four different backgrounds, two lighting conditions, and various angles are used for capturing the images of dry fruit. The Dry Fruit Image Dataset was created to include high-quality images of major dry fruits that are consumed and exported. It consists of four types of dry fruit each, namely, Almond, Cashew, Dried Fig, and Raisins. Each type of dry fruit is further categorized into three major subclasses. Almond has three subclasses namely, Regular, Sanora, and Mamra. Cashew has subclasses namely, Regular, Special, and Jumbo. Raisin has subclasses namely, Black, Grade 1, and Premium. Fig has subclasses namely, Small, Medium, and Jumbo. Hence, a total of 12 different classes are contained in the dataset. The dry fruits were taken in various lighting conditions and backgrounds, namely, artificial light and natural light, while the backgrounds included white, black, green, and human palms. Data collection took place in February and March. In the VIIT lab, typical images were taken in a variety of lighting, background, and angle situations. The dataset utilized in this study comprises two primary light sources: Natural Sunlight and Artificial light. Natural Sunlight served as the natural light source, with a range of sunlight angles spanning from 60° to 120°. Additionally, two LEDs were employed as the Artificial light sources. Images were pre-processed using a Python script and Microsoft Power Automate. The dimensions of the images, 512 × 512 make it easier to build object classification models.

Experimental Design Summary (PPET_10000000018)

The dry fruit images were captured using two different makes of camera, that were Apple's iPhone 13 and Motorola's Moto G40 fusion mobiles' rear camera having high resolution. In all, 11500+ images were captured with a camera and then stored in various folders according to their category and classification. Four different backgrounds, two lighting conditions, and various angles are used for capturing the images of dry fruit. The Dry Fruit Image Dataset was created to include high-quality images of major dry fruits that are consumed and exported. It consists of four types of dry fruit each, namely, Almond, Cashew, Dried Fig, and Raisins. Each type of dry fruit is further categorized into three major subclasses. Almond has three subclasses namely, Regular, Sanora, and Mamra. Cashew has subclasses namely, Regular, Special, and Jumbo. Raisin has subclasses namely, Black, Grade 1, and Premium. Fig has subclasses namely, Small, Medium, and Jumbo. Hence, a total of 12 different classes are contained in the dataset. The dry fruits were taken in various lighting conditions and backgrounds, namely, artificial light and natural light, while the backgrounds included white, black, green, and human palms. Data collection took place in February and March. In the VIIT lab, typical images were taken in a variety of lighting, background, and angle situations. The dataset utilized in this study comprises two primary light sources: Natural Sunlight and Artificial light. Natural Sunlight served as the natural light source, with a range of sunlight angles spanning from 60° to 120°. Additionally, two LEDs were employed as the Artificial light sources. Images were pre-processed using a Python script and Microsoft Power Automate. The dimensions of the images, 512 × 512 make it easier to build object classification models.

Acquired Images Annotation Description (PPET_10000000018)
After the survey in the local stores and wholesaler market, all twelve classes of dry fruits i.e. Almond Mamra, Almond Regular, Almond Sanora, Cashew Jumbo, Cashew Regular, Cashew Special, Fig Jumbo, Fig Medium, Fig Small, Raisin Black, Raisin Grade 1, Raisin Premium, were purchased from PUNE, INDIA. The photographs are taken under a range of environmental circumstances, including various lighting situations and backgrounds shot from various viewpoints. All of the images were arranged in the following order: almond, cashew, fig, and sultana. There are three separate folders for each category/grade of dry fruit, such as Mamra, Sanora, Regular for Almond, and so on.

Acquired Images Annotation Description (PPET_10000000018)

After the survey in the local stores and wholesaler market, all twelve classes of dry fruits i.e. Almond Mamra, Almond Regular, Almond Sanora, Cashew Jumbo, Cashew Regular, Cashew Special, Fig Jumbo, Fig Medium, Fig Small, Raisin Black, Raisin Grade 1, Raisin Premium, were purchased from PUNE, INDIA. The photographs are taken under a range of environmental circumstances, including various lighting situations and backgrounds shot from various viewpoints. All of the images were arranged in the following order: almond, cashew, fig, and sultana. There are three separate folders for each category/grade of dry fruit, such as Mamra, Sanora, Regular for Almond, and so on.

Table 4. The experiments registered under this study are as follows:

Sample ID	Experiment Type ID	Experiment ID	Image type (Original / Derived / Unknown)	Any Other Information	Light Source	Camera Specifications	Images Resolution (in MP)	Artificial Light Source	Camera Used to Capture Images	Image Background Colour	LEDs Light Position	Original Images Size (in pixels)	Scaled Images Size (in pixels)
PPSM_10000248555	PPET_10000000018	PPE_10000219823	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000248676	PPET_10000000018	PPE_10000219944	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000248797	PPET_10000000018	PPE_10000220065	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000248918	PPET_10000000018	PPE_10000220186	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249039	PPET_10000000018	PPE_10000220307	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249160	PPET_10000000018	PPE_10000220428	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249281	PPET_10000000018	PPE_10000220549	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249402	PPET_10000000018	PPE_10000220670	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249523	PPET_10000000018	PPE_10000220791	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249644	PPET_10000000018	PPE_10000220912	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249765	PPET_10000000018	PPE_10000221033	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000249886	PPET_10000000018	PPE_10000221154	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250007	PPET_10000000018	PPE_10000221275	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250128	PPET_10000000018	PPE_10000221396	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250239	PPET_10000000018	PPE_10000221507	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250360	PPET_10000000018	PPE_10000221628	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250481	PPET_10000000018	PPE_10000221749	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250602	PPET_10000000018	PPE_10000221870	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250723	PPET_10000000018	PPE_10000221991	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512
PPSM_10000250844	PPET_10000000018	PPE_10000222112	Derived	N/A	Artificial/Natural	Apple iPhone 13 (12-megapixel, back camera)/Motorola Moto G40 Fusion (64-megapixel, rear camera)	N/A	LED	Back/Rear	Black/White/Green/Human Palm	Two LEDs were positioned at a 45° angle relative to the surface of the background setup, one on each side.	3042×4032	512×512

Experiment ID	Image File Name (with path)	Image Preview	Image Size
PPE_10000224262	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_921.jpg	Download Image	20K
PPE_10000224263	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_922.jpg	Download Image	20K
PPE_10000224264	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_923.jpg	Download Image	20K
PPE_10000224265	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_924.jpg	Download Image	20K
PPE_10000224266	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_925.jpg	Download Image	20K
PPE_10000224267	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_926.jpg	Download Image	20K
PPE_10000224268	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_927.jpg	Download Image	16K
PPE_10000224269	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_928.jpg	Download Image	20K
PPE_10000224270	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_929.jpg	Download Image	20K
PPE_10000224271	DRY_FRUIT_IMAGE_DATASET/CASHEW/CASHEW_SPECIAL/CASHEW_SPECIAL_930.jpg	Download Image	20K

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