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

Online extraction-LC-MS/MS is an alternative imaging tool for spatial-resolved metabolomics: Mint leaf as a pilot study

An attempt was made here to a complemental analytical tool for classical MSI approach. OLE-LC-MS/MS imaging was proposed to plot the spatial-resolved metabolome through deploying mint leaf as a proof-of-concept. A dried leaf underwent chemical composition characterization using OLE-LC-Qtof-MS. Another dried leaf was cut into small pieces, and all pieces were successively packed into a suitable cartridge to undergo OLE-LC-SRM measurements. Fifty-two compounds were observed and identified. Special attention was paid onto isomeric identification using fragment ion intensity ranking style, e.g., 3-O-caffeoylquinic acid vs. 4-O-caffeoylquinic acid. Thereof, 23 abundant ones were involved for relatively quantitative analysis. Quantitative settings were optimized using online ER-MS program. Following spatial metabolome imaging, regioselective distributions were observed for most concerned metabolites. Particularly, isomer-specific occurrences were observed for luteolin-7-O-glucuronide and luteolin-3'-O-glucuronide. Together, OLE-LC-MS/MS is alternative for spatial metabolome imaging due to the advantages at isomeric separation, identification confidence, and quantitative accuracy.

BanglaVeg: A curated vegetable image dataset from Bangladesh for precision agriculture

Vegetables are one of the most essential parts of the agricultural sector and the food supply chain; therefore, the identification and categorization of vegetable types require effective strategies. In this paper, we introduce the Vegetable Image Dataset, which is a meticulously developed collection of 4319 images representing 12 different vegetable species native to Bangladesh, including Potato, Onion, Green Chili, Garlic, Radish, Bean, Ladies Finger, Cucumber, Bitter Melon, Brinjal (Eggplant), Tomato, Pointed Gourd. The dataset contains images taken in natural environments, including local markets, agricultural fields, and homes, using phone cameras to represent real-world conditions better. All photos have undergone background removal and annotation to highlight features such as shape, texture, and color, thus making it a handy resource for deep-learning projects. Developed primarily for developing convolutional neural network (CNN) models, this dataset allows for the automatic identification and classification of vegetables for various applications. Applications range from improving the supply chain for agriculture to allowing instantaneous detection of vegetables in kitchens or marketplaces and increasing the efficiency of automation for sorting and packaging. With its unique characteristic of Bangladeshi vegetables, this dataset provides the valuable resource needed for improving agricultural practices using AI-driven ways and fostering further developments of technologies in underserved communities.

A comprehensive hog plum leaf disease dataset for enhanced detection and classification

A comprehensive Hog plum leaf disease dataset is greatly needed for agricultural research, precision agriculture, and efficient management of disease. It will find applications toward the formulation of machine learning models for early detection and classification of disease, thus reducing dependency on manual inspections and timely interventions. Such a dataset provides a benchmark for training and testing algorithms, further enhancing automated monitoring systems and decision-support tools in sustainable agriculture. It enables better crop management, less use of chemicals, and more focused agronomical practices. This dataset will contribute to the global research being carried out for the advancement of disease-resistant plant strategy development and efficient management practices for better agricultural productivity along with sustainability. These images have been collected from different regions of Bangladesh. In this work, two classes were used: 'Healthy' and 'Insect hole', representing different stages of disease progression. The augmentation techniques that involve flipping, rotating, scaling, translating, cropping, adding noise, adjusting brightness, adjusting contrast, and scaling expanded a dataset of 3782 images to 20,000 images. These have formed very robust deep learning training sets, hence better detection of the disease.

Dataset of Centella Asiatica leaves for quality assessment and machine learning applications

Centella asiatica is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of Centella asiatica 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. To address this quality control issue using machine learning applications, we have developed an image dataset of Centella asiatica leaves. The images were captured using Samsung Galaxy M21 mobile phones and depict the leaves in "Dried," "Healthy," and "Unhealthy" states. These states are further divided into "Single" and "Multiple" leaves categories, with "Single" leaves being further classified into "Front" and "Back" views to facilitate a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images. This dataset is instrumental in the development and evaluation of image recognition algorithms, serving as a foundational resource for computer vision research. Moreover, it provides a valuable platform for testing and validating algorithms in areas such as image categorization and object detection. For researchers exploring the medicinal potential of Centella asiatica in traditional medicine, this dataset offers critical information on the plant's health, thereby advancing research in herbal medicine and ethnopharmacology.

Facilitating spice recognition and classification: An image dataset of Indian spices

This data paper presents a comprehensive visual dataset of 19 distinct types of Indian spices, consisting of high-quality images meticulously curated to facilitate various research and educational applications. The dataset includes extensive imagery of the following spices: Asafoetida, Bay Leaf, Black Cardamom, Black Pepper, Caraway Seeds, Cinnamon Stick, Cloves, Coriander Seeds, Cubeb Pepper, Cumin Seeds, Dry Ginger, Dry Red Chilly, Fennel Seeds, Green Cardamom, Mace, Nutmeg, Poppy Seeds, Star Anise, and Stone Flowers. Each image in the dataset has been captured under controlled conditions to ensure consistency and clarity, making it an invaluable resource for studies in food science, agriculture, and culinary arts. The dataset can also support machine learning and computer vision applications, such as spice recognition and classification. By providing detailed visual documentation, this dataset aims to promote a deeper understanding and appreciation of the rich diversity of Indian spices.

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. Captured using an iPhone 13 and Mi10T mobile phones, the images exhibit diverse climatic conditions, ranging from daytime to night-time scenarios. Subsequent to image acquisition, a pre-processing phase was undertaken to standardize the dataset. This involved renaming the images and adjusting their dimensions to a uniform 768 × 576 resolution, after which they were organized into respective folders. The uniqueness of this dataset lies in its incorporation of diverse environmental conditions, comprehensive helmet types, variability in helmet orientations, and its status as a large and balanced dataset, thereby presenting a realistic representation of real-world scenarios. The dataset's utility extends to various machine learning tasks, including image classification, object detection, and pose estimation specifically geared towards helmet recognition. Its scientific value lies in its potential to advance research and development in the realm of safety measures associated with motorcycle helmet usage.

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. Additionally, the dataset facilitates investigations into the medicinal properties of Thai cannabis. Interdisciplinary collaboration is encouraged, providing opportunities for innovative insights spanning biology, horticulture, and data science. Beyond fundamental research, this dataset holds practical implications for agriculture, technology development, and disease prevention, offering insights into both dried and live states of Thai cannabis plants across various strains.

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

This article introduces a dataset of 10,042 Lemongrass (Cymbopogon citratus) 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 images underwent pre-processing, involving batch image resizing through FastStone Photo Resizer and subsequent operations for compatibility with pre-trained models using the 'preprocess_input' function in the Keras library. The significance of the Lemongrass Leaves Dataset was demonstrated through experiments using well-known pre-trained models, such as InceptionV3, Xception, and MobileNetV2, showcasing its potential to enhance machine learning model accuracy in Lemongrass leaf identification and disease detection. Our goal is to aid researchers, farmers, and enthusiasts in improving Lemongrass cultivation and disease prevention. Researchers can use this dataset to train machine learning models for leaf condition classification, while farmers can monitor their crop's health. Its authenticity and size make it valuable for projects enhancing Lemongrass cultivation, boosting crop yield, and preventing diseases. This dataset is a significant step toward sustainable agriculture and plant health management.

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

The Plumbago Zeylanica (Chitrak) 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 Chitrak, a species of paramount importance in traditional medicine and environmental contexts. Researchers and practitioners can benefit from this dataset's richness in terms of both quantity and quality, using it to develop and test algorithms for leaf classification and health assessment. The Chitrak leaf image dataset holds the potential to foster innovative investigations and applications within the domains of botany, medicine, and environmental sciences.