This dataset contains a curated collection of leaf images from seven commonly used medicinal plant species native to Bangladesh, developed to support research in plant identification and classification using computer vision and machine learning techniques. The central research hypothesis is that leaf morphology provides sufficient visual information to discriminate medicinal plant species under real-world outdoor conditions.The dataset comprises a total of 13,095 leaf images, including 6,700 raw images and 6,395 pre-processed images. All images are resized to 640 × 640 pixels. However, the images represent seven medicinal plant species widely used in traditional and modern healthcare practices in Bangladesh. The data show consistent intra-class visual characteristics such as leaf shape, venation patterns, texture, margin structure, and colour distribution while exhibiting clear inter-species differences that support reliable species-level classification and comparative analysis.All images were manually collected from natural outdoor environments, including agricultural fields, nurseries, and home gardens located in the Akran Bazar, Ashulia, and Savar regions of Dhaka, Bangladesh. This real-world acquisition approach preserves natural variations in illumination, background complexity, leaf orientation, scale, and growth stages, making the dataset suitable for developing and evaluating robust recognition models under practical conditions.The dataset is organized into two main folders. The Raw Images folder contains original, unaltered images without background removal or enhancement, preserving authentic acquisition conditions. The Pre-processed Images folder includes processed versions of the raw images to facilitate standardized experimentation; preprocessing steps may include resizing, normalization, and noise reduction, as documented. Images in both folders are arranged in class-wise subdirectories corresponding to each plant species.All images are provided in commonly used formats, allowing researchers to apply custom preprocessing, feature extraction, and modelling strategies. The dataset can serve as a benchmark resource for evaluating classification robustness and generalization performance. In addition, it enables comparative studies across different algorithms, supports dataset augmentation research, and facilitates reproducible experimentation. The dataset is suitable for academic research, educational purposes, and applied studies in medicinal plant identification, biodiversity conservation, agricultural technology, and healthcare-oriented artificial intelligence research, particularly in regions where publicly available medicinal plant datasets remain limited.

This dataset contains a curated collection of leaf images from seven commonly used medicinal plant species native to Bangladesh, developed to support research in plant identification and classification using computer vision and machine learning techniques. The central research hypothesis is that leaf morphology provides sufficient visual information to discriminate medicinal plant species under real-world outdoor conditions.The dataset comprises a total of 13,095 leaf images, including 6,700 raw images and 6,395 pre-processed images. All images are resized to 640 × 640 pixels. However, the images represent seven medicinal plant species widely used in traditional and modern healthcare practices in Bangladesh. The data show consistent intra-class visual characteristics such as leaf shape, venation patterns, texture, margin structure, and colour distribution while exhibiting clear inter-species differences that support reliable species-level classification and comparative analysis.All images were manually collected from natural outdoor environments, including agricultural fields, nurseries, and home gardens located in the Akran Bazar, Ashulia, and Savar regions of Dhaka, Bangladesh. This real-world acquisition approach preserves natural variations in illumination, background complexity, leaf orientation, scale, and growth stages, making the dataset suitable for developing and evaluating robust recognition models under practical conditions.The dataset is organized into two main folders. The Raw Images folder contains original, unaltered images without background removal or enhancement, preserving authentic acquisition conditions. The Pre-processed Images folder includes processed versions of the raw images to facilitate standardized experimentation; preprocessing steps may include resizing, normalization, and noise reduction, as documented. Images in both folders are arranged in class-wise subdirectories corresponding to each plant species.All images are provided in commonly used formats, allowing researchers to apply custom preprocessing, feature extraction, and modelling strategies. The dataset can serve as a benchmark resource for evaluating classification robustness and generalization performance. In addition, it enables comparative studies across different algorithms, supports dataset augmentation research, and facilitates reproducible experimentation. The dataset is suitable for academic research, educational purposes, and applied studies in medicinal plant identification, biodiversity conservation, agricultural technology, and healthcare-oriented artificial intelligence research, particularly in regions where publicly available medicinal plant datasets remain limited.

Description:This dataset presents a collection of carefully annotated images of seven commonly found tropical flower species, aimed at advancing the capabilities of machine learning and computer vision models in flower detection, classification, and recognition. Collected with the intent to capture diverse environmental contexts, this dataset offers a unique opportunity for researchers and practitioners in botany, agriculture, ecology, and AI to study tropical flowers in various.Dataset Content:This dataset leverages a comprehensive dataset comprising 4,319 images of seven tropical flower species, with variability in backgrounds, lighting conditions, and growth stages to provide comprehensive data diversity meticulously curated to support machine learning applications in automated species identification and ecological monitoring. The dataset captures diverse natural settings and various stages of flower development, ensuring a robust foundation for image-based classification and detection tasks. 1. Rose: 827 images2. Bougainvillea: 580 images3. Marigold: 717 images4. Hibiscus: 548 images5. Crown of Thorns: 583 images6. Jungle Geranium: 698 images7. Madagascar Periwinkle: 366 imagesPurpose:The purpose of this dataset is to help create machine learning models that accurately recognize and classify tropical flowers, aiding in biodiversity studies and education about plant species.

Description:This dataset presents a collection of carefully annotated images of seven commonly found tropical flower species, aimed at advancing the capabilities of machine learning and computer vision models in flower detection, classification, and recognition. Collected with the intent to capture diverse environmental contexts, this dataset offers a unique opportunity for researchers and practitioners in botany, agriculture, ecology, and AI to study tropical flowers in various.Dataset Content:This dataset leverages a comprehensive dataset comprising 4,319 images of seven tropical flower species, with variability in backgrounds, lighting conditions, and growth stages to provide comprehensive data diversity meticulously curated to support machine learning applications in automated species identification and ecological monitoring. The dataset captures diverse natural settings and various stages of flower development, ensuring a robust foundation for image-based classification and detection tasks. 1. Rose: 827 images2. Bougainvillea: 580 images3. Marigold: 717 images4. Hibiscus: 548 images5. Crown of Thorns: 583 images6. Jungle Geranium: 698 images7. Madagascar Periwinkle: 366 imagesPurpose:The purpose of this dataset is to help create machine learning models that accurately recognize and classify tropical flowers, aiding in biodiversity studies and education about plant species.

This dataset comprises detailed clinical, physiological, and historical health information collected from maternal patients to evaluate potential health risks during pregnancy. It serves as a resource for developing predictive models aimed at identifying and managing high-risk pregnancies, providing insights into maternal health factors, and supporting personalized patient care. The dataset is well-suited for research in obstetrics, predictive health modeling, and maternal healthcare management.Key Features:Age: Age of the patient, which can be a significant factor in pregnancy risk.Systolic BP: Systolic blood pressure, indicating the force exerted on artery walls when the heart beats. Elevated levels can indicate hypertension.Diastolic: Diastolic blood pressure, measuring pressure between heartbeats, where high values can be a sign of gestational hypertension or preeclampsia risk.BS (Blood Sugar): Blood sugar level of the patient, crucial for monitoring conditions such as gestational diabetes, which can affect fetal and maternal health.Body Temp: Patient’s body temperature, which can help identify infection or inflammation.BMI (Body Mass Index): A measure of body fat based on height and weight. Higher BMI values can be associated with complications like gestational diabetes and hypertension.Previous Complications: Binary indicator (0 or 1) for previous pregnancy complications, which could predispose patients to future risks.Preexisting Diabetes: Indicates whether the patient has a history of diabetes, an essential factor as it raises the risk for complications.Gestational Diabetes: Presence of diabetes developed during pregnancy, a significant risk factor for both mother and child.Mental Health: Indicator of mental health issues, which may affect pregnancy outcomes and maternal wellbeing.Heart Rate: Heart rate of the patient, which, when elevated, may indicate stress or cardiovascular strain.Risk Level: Categorized risk level (e.g., High, Low), assessing the overall health risk based on the patient’s profile.Applications:This dataset is highly applicable in:Risk Stratification: Helping healthcare providers assess which patients are at higher risk for complications.Predictive Modeling: Facilitating machine learning and statistical models to forecast health risks and inform preventive measures.Maternal Health Research: Supporting studies focused on the impact of various health metrics on pregnancy outcomes.Healthcare Policy: Providing evidence to develop guidelines for maternal healthcare, especially in populations with limited resources.This dataset is an invaluable tool for professionals in obstetrics, public health, and predictive healthcare analytics, aimed at improving the quality of maternal care and optimizing health outcomes for mothers and infants.

This dataset comprises detailed clinical, physiological, and historical health information collected from maternal patients to evaluate potential health risks during pregnancy. It serves as a resource for developing predictive models aimed at identifying and managing high-risk pregnancies, providing insights into maternal health factors, and supporting personalized patient care. The dataset is well-suited for research in obstetrics, predictive health modeling, and maternal healthcare management.Key Features:Age: Age of the patient, which can be a significant factor in pregnancy risk.Systolic BP: Systolic blood pressure, indicating the force exerted on artery walls when the heart beats. Elevated levels can indicate hypertension.Diastolic: Diastolic blood pressure, measuring pressure between heartbeats, where high values can be a sign of gestational hypertension or preeclampsia risk.BS (Blood Sugar): Blood sugar level of the patient, crucial for monitoring conditions such as gestational diabetes, which can affect fetal and maternal health.Body Temp: Patient’s body temperature, which can help identify infection or inflammation.BMI (Body Mass Index): A measure of body fat based on height and weight. Higher BMI values can be associated with complications like gestational diabetes and hypertension.Previous Complications: Binary indicator (0 or 1) for previous pregnancy complications, which could predispose patients to future risks.Preexisting Diabetes: Indicates whether the patient has a history of diabetes, an essential factor as it raises the risk for complications.Gestational Diabetes: Presence of diabetes developed during pregnancy, a significant risk factor for both mother and child.Mental Health: Indicator of mental health issues, which may affect pregnancy outcomes and maternal wellbeing.Heart Rate: Heart rate of the patient, which, when elevated, may indicate stress or cardiovascular strain.Risk Level: Categorized risk level (e.g., High, Low), assessing the overall health risk based on the patient’s profile.Applications:This dataset is highly applicable in:Risk Stratification: Helping healthcare providers assess which patients are at higher risk for complications.Predictive Modeling: Facilitating machine learning and statistical models to forecast health risks and inform preventive measures.Maternal Health Research: Supporting studies focused on the impact of various health metrics on pregnancy outcomes.Healthcare Policy: Providing evidence to develop guidelines for maternal healthcare, especially in populations with limited resources.This dataset is an invaluable tool for professionals in obstetrics, public health, and predictive healthcare analytics, aimed at improving the quality of maternal care and optimizing health outcomes for mothers and infants.