Dataset of leaves for quality assessment and machine learning applications.

is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of leaves are triterpenoid saponins, which are predominantly believed to be responsible for its therapeutic properties. Ensuring the use of high-quality leaves in herbal medicine preparation is crucial across all medicinal practices.

Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes.

This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization.

Exploring the natural pothole dataset generated by the abrasion and cavitation effects of river water on rocks.

The Natural Pothole Dataset within River Environments is an extensive collection of 3992 high-resolution images [1] documenting various natural potholes located in riverine settings. Each image has been rigorously annotated utilizing the YOLO (You Only Look Once) object detection framework, which ensures precise bounding box coordinates and accurate class labels for identified potholes. The annotations are provided in XML format, facilitating seamless integration with machine learning algorithms and computer vision applications.

HelmetML: A dataset of helmet images for machine learning applications.

The improper wearing or absence of helmets represents a significant contributing factor to fatal accidents in motorcycle driving. This dataset serves the purpose of detecting whether individuals have correctly or incorrectly worn helmets through camera-based analysis. The Helmet dataset has been curated, comprising a total of 28,736 images featuring various helmet types, including Full-Face, Half-Face, Modular, and Off-Road Helmets, in both correct and incorrect configurations.

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category.

A comprehensive dataset of eight Thai cannabis classes for botanical exploration.

This dataset presents a comprehensive collection of images representing both dried and live samples from eight distinct Thai cannabis classes. The dataset includes a total of 14,094 images, with images depicting dried and healthy specimens. These images serve as a valuable resource for researchers engaged in botanical exploration, machine learning, and computer vision studies.

A comprehensive lemongrass () leaf dataset for agricultural research and disease prevention.

This article introduces a dataset of 10,042 Lemongrass () leaf images, captured with high quality camera of a mobile phone in real-world conditions. The dataset classifies leaves as "Dried," "Healthy," or "Unhealthy," making it useful for machine learning, agriculture research, and plant health analysis. We collected the plant leaves from the Vishwakarma University Pune herbal garden and the captured the images in diverse backgrounds, angles, and lighting conditions.

The image dataset of Indian coins: A machine learning approach for Indian currency.

In an increasingly digital world, the significance of creating a Comprehensive Image Dataset of Contemporary Indian Coins (CIDCIC) cannot be overstated. This research presents a dataset comprising 6,672 images of 53 different classes of Indian coins, including denominations of 25 Paisa, 50 Paisa, 1 Rupee, 2 Rupee, 5 Rupee, 10 Rupee, and 20 Rupee. The images of coins with various shapes and sizes are taken from obverse and reverse sides in various environments and different backgrounds.

Empowering agricultural research: A comprehensive custard apple () disease dataset for precise detection.

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants.

() leaf image dataset: A comprehensive collection for botanical studies, herbal medicine research, and environmental analyses.

The () Leaf Image Dataset is a valuable resource for botanical studies, herbal medicine research, and environmental analyses. Comprising a total of 10,660 high-resolution leaf images, the dataset is meticulously categorized into three distinct classes: Unhealthy leaves (3343 images), Healthy leaves (5288 images), and Dried leaves (2029 images). These images were captured from the medicinal plant , a species of paramount importance in traditional medicine and environmental contexts.

Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical.

Mint leaves: Dried, fresh, and spoiled dataset for condition analysis and machine learning applications.

We present a comprehensive dataset of 5,323 images of mint (pudina) leaves in various conditions, including dried, fresh, and spoiled. The dataset is designed to facilitate research in the domain of condition analysis and machine learning applications for leaf quality assessment. Each category of the dataset contains a diverse range of images captured under controlled conditions, ensuring variations in lighting, background, and leaf orientation.

Coconut () tree disease dataset: A dataset for disease detection and classification for machine learning applications.

The ``Coconut () Tree Disease Dataset'' comprises 5,798 images across five disease categories: ``Bud Root Dropping,'' ``Bud Rot,'' ``Gray Leaf Spot,'' ``Leaf Rot,'' and ``Stem Bleeding.'' This dataset is intended for machine learning applications, facilitating disease detection and classification in coconut trees. The dataset's diversity and size make it suitable for training and evaluating disease detection models.

A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification.

Detecting authentic and quality banknotes presents a significant challenge, particularly for individuals with low vision or visual impairments. Extensive research has been dedicated to achieving accurate banknote detection. It is crucial for clean banknotes to be readily detectable and accepted in daily transactions.

Sensor based dataset to assess the impact of urban heat island effect mitigation and indoor thermal comfort via terrace gardens.

This dataset contains temperature variations observed on a building terrace that is partially covered with plantations on one side while the other side remains exposed. The study was conducted at a shelter named "Anbagham" in Tamil Nadu, India. Two sets of temperature and humidity sensors were utilized, with one set placed on the external roofs and the other set placed inside the rooms corresponding to these roofs.

Dataset of cannabis seeds for machine learning applications.

The recent changes in policies in several countries regarding cannabis use has increased cannabis usage and research [1,2]. Cannabis is the second most used psychoactive substance word-wide [3]. Cannabis remains the subject of many research works.

VegNet: Dataset of vegetable quality images for machine learning applications.

The agricultural industry has an unmet requirement for quick and accurate classification or recognition of vegetables according to the quality criteria. This open research problem draws attention to the research scholars every time. The classification and object detection challenges have seen highly encouraging outcomes from machine learning and deep learning techniques.

Dataset of Indian and Thai banknotes with annotations.

Multinational banknote detection in real time environment is the open research problem for the research community. Several studies have been conducted for providing solution for fast and accurate recognition of banknotes, detection of counterfeit banknotes, and identification of damaged banknotes. The State-of art techniques like machine learning (ML) and deep learning (DL) are dominating the traditional methods of digital image processing technique used for banknote classification.