The dataset contains a total image count of 10,361. find more This dataset offers a robust platform for training and validating deep learning and machine learning algorithms designed to classify and recognize groundnut leaf diseases. The critical process of recognizing plant diseases is essential to prevent crop losses, and our dataset will prove beneficial for identifying diseases in groundnut plants. Public access to this dataset is granted at the link: https//data.mendeley.com/datasets/22p2vcbxfk/3. At https://doi.org/10.17632/22p2vcbxfk.3, and.
The practice of utilizing medicinal plants for therapeutic purposes has ancient origins. The raw materials employed in the production of herbal medicine are commonly recognized as medicinal plants [2]. The U.S. Forest Service's assessment, detailed in reference [1], suggests that plants are the source of 40% of pharmaceutical drugs in use in the Western world. The modern pharmacopeia contains seven thousand medicinal compounds, each having origins in plant life. By blending traditional empirical knowledge with modern science, herbal medicine achieves a unique approach [2]. Blood and Tissue Products A medicinal plant stands as a vital preventative measure against a multitude of illnesses [2]. The extraction of the essential medicine component is undertaken from different parts of the plant [8]. As a substitute for pharmaceutical medications, medicinal plants are frequently employed in nations with limited economic development. An assortment of plant species exists on this planet. Different shapes, colors, and leaf structures are found in herbs, one of the many examples [5]. Ordinary individuals face difficulty in identifying these herb varieties. Across the globe, medicinal applications leverage more than fifty thousand distinct plant species. According to [7], 8000 medicinal plants native to India exhibit proven medicinal properties. Automatic classification of these plant species is of paramount importance, as manual classification demands specialized knowledge of the species' characteristics. Medicinal plant species identification from photographs, using machine learning methods, is a complex but compelling endeavor for the academic community. Disease transmission infectious Reference [4] highlights the dependence of Artificial Neural Network classifiers' performance on the quality of their associated image dataset. Ten different Bangladeshi plant species, including their medicinal properties, are represented in this article's image dataset. The Pharmacy Garden at Khwaja Yunus Ali University and the Khwaja Yunus Ali Medical College & Hospital in Sirajganj, Bangladesh, were among the gardens that provided images of leaves from medicinal plants. High-resolution images were captured by employing mobile phone cameras. Five hundred images of each of these ten medicinal species – Nayantara (Catharanthus roseus), Pathor kuchi (Kalanchoe pinnata), Gynura procumbens (Longevity spinach), Bohera (Terminalia bellirica), Haritaki (Terminalia chebula), Thankuni (Centella asiatica), Neem (Azadirachta indica), Tulsi (Ocimum tenniflorum), Lemon grass (Cymbopogon citratus), and Devil backbone (Euphorbia tithymaloides) – are part of the data collection. The benefits of this dataset are numerous for researchers employing machine learning and computer vision algorithms. The core components of this research include training and testing machine learning models with a carefully assembled high-quality dataset, the creation of new computer vision algorithms, automating medicinal plant identification in the domain of botany and pharmacology to facilitate drug discovery and preservation, and data augmentation techniques. Researchers in machine learning and computer vision can leverage this medicinal plant image dataset to develop and evaluate algorithms for plant phenotyping, disease detection, plant identification, drug development, and other tasks related to medicinal plants, thereby gaining a valuable resource.
The motion of the vertebrae, both individually and collectively as the spine, has a substantial correlation to spinal function. Comprehensive kinematic data sets are required for the systematic evaluation of individual movements. In addition, the information should facilitate comparisons of inter- and intraindividual variations in vertebral positioning during specialized movements like walking. Surface topography (ST) data are included in this article, collected from individuals walking on a treadmill at varying speeds: 2 km/h, 3 km/h, and 4 km/h. Ten complete strides of walking were incorporated into each test recording, permitting a comprehensive investigation of motion patterns. Volunteers without symptoms or pain are the focus of the provided data. The data sets contain the vertebral orientation's data in all three motion directions for the vertebra prominens through L4, along with pelvic data. Besides other data, spinal attributes, such as balance, slope, and lordosis/kyphosis parameters, are also considered, along with the allocation of motion data within specific gait cycles. The raw data, in its unprocessed entirety, is supplied. For the purpose of recognizing characteristic motion patterns and variations in vertebral motion across individuals and within an individual, a wide spectrum of subsequent signal processing and assessment techniques can be employed.
In the past, the task of manually preparing datasets was both time-consuming and demanding in terms of the required effort. Employing web scraping, another data acquisition method was tried. Web scraping tools contribute to the creation of numerous data errors. Because of this, we developed Oromo-grammar, a novel Python package. This package accepts raw text files from users, isolates every potential root verb from the provided text, and appends each of these to a Python list. The algorithm then processes each root verb in the list to produce its corresponding stem list. Ultimately, our algorithm constructs grammatical phrases employing the correct affixations and personal pronouns. The generated phrase dataset serves as an indicator of grammatical features, including number, gender, and case. This grammar-rich dataset is applicable to cutting-edge NLP applications, including machine translation, sentence completion, and grammar/spell checking tools. The dataset provides valuable resources for language grammar instruction, aiding linguists and academics alike. A methodical approach to analyzing and subtly adjusting the algorithm's affix structures enables easy reproduction of this method in other languages.
This paper details CubaPrec1, a daily precipitation dataset for Cuba, 1961-2008, featuring a high-resolution (-3km) gridded format. From the 630 station data series of the National Institute of Water Resources network, the dataset was assembled. The process of quality control for the original station data series involved evaluating spatial coherence, and missing values were individually estimated by day and site. A grid with a 3×3 km resolution was established, using the full data series, to estimate daily precipitation and their uncertainty at each grid box. Precisely capturing the spatiotemporal characteristics of precipitation in Cuba, this new product establishes a helpful basis for future studies within hydrology, climatology, and meteorology. The data described in the collection is hosted on Zenodo, accessible via this DOI: https://doi.org/10.5281/zenodo.7847844.
The addition of inoculants to precursor powder is a technique for influencing the growth of grains during the manufacturing process. Niobium carbide (NbC) particles were incorporated into IN718 gas atomized powder for additive manufacturing using laser-blown powder directed energy deposition (LBP-DED). The gathered data from this research provides insights into the effects of NbC particles on the grain structure, texture, elastic properties, and oxidative properties of LBP-DED IN718, investigated under as-deposited and post-heat treatment conditions. Employing a multifaceted approach encompassing X-ray diffraction (XRD), scanning electron microscopy (SEM) with electron backscattered diffraction (EBSD), and transmission electron microscopy (TEM) combined with energy dispersive X-ray spectroscopy (EDS), the microstructure was thoroughly examined. By means of resonant ultrasound spectroscopy (RUS), the elastic properties and phase transitions of materials undergoing standard heat treatments were ascertained. Thermogravimetric analysis (TGA) serves to scrutinize the oxidative characteristics at a temperature of 650°C.
In the semi-arid regions of central Tanzania, groundwater is a vital supply of water for drinking and agricultural irrigation. Degradation of groundwater quality results from the combined effects of anthropogenic and geogenic pollution. The release of contaminants from human activities, a characteristic of anthropogenic pollution, can seep into and pollute groundwater through the process of leaching. Mineral rock presence and dissolution are instrumental in determining the extent of geogenic pollution. Aquifers enriched with carbonates, feldspars, and mineral content frequently show a high degree of geogenic pollution. Drinking water tainted with pollutants from groundwater carries significant health risks. Ultimately, safeguarding public health mandates assessing groundwater to determine a consistent pattern and geographic distribution of groundwater pollution. A literature survey failed to identify any publications detailing the geographical pattern of hydrochemical parameters within central Tanzania. Situated within the East African Rift Valley and the Tanzania craton, central Tanzania comprises the Dodoma, Singida, and Tabora regions. This article incorporates a dataset of pH, electrical conductivity (EC), total hardness (TH), Ca²⁺, Mg²⁺, HCO₃⁻, F⁻, and NO₃⁻ measurements from 64 groundwater samples; these samples were collected from the Dodoma region (22), Singida region (22), and Tabora region (20). Data collection, encompassing 1344 km, was geographically segmented into east-west routes via B129, B6, and B143; and north-south routes through A104, B141, and B6. A model depicting the geochemistry and spatial variations of physiochemical parameters across these three regions can be developed using this dataset.