This dataset contains detailed clinical records of 1,106 patients collected from Bangladesh between October 27, 2024 and January 2, 2025. Typhoid fever is a serious bacterial infection caused by Salmonella Typhi, which is commonly spread through contaminated food and water. It remains a widespread health issue in many parts of Bangladesh, especially where sanitation and clean water access are limited. The dataset includes results from Widal tests, which are commonly used to detect typhoid and paratyphoid fever. These include antigens such as TO and TH (specific to Salmonella Typhi) and AH and BH (specific to Salmonella Paratyphi). Additionally, the dataset contains values from the Weil-Felix test, including OXK, OX2, and OX19, which are used to help identify rickettsial infections — a group of bacterial infections often presenting with similar symptoms. This dataset has been validated by a certified medical professional and is suitable for use in epidemiological research, diagnostic modeling, and public health analysis. 1. Weil-Felix Test (Rickettsial Infections): OXK, OX2, OX19→ Antigens used to detect antibodies against rickettsial infections (e.g., scrub typhus, epidemic typhus).Note: Weil-Felix is nonspecific and largely replaced by serological/PCR tests in modern labs.2. Widal Test (Typhoid/Paratyphoid Fever): TO (Typhi O), TH (Typhi H), AH (Paratyphi A H), BH (Paratyphi B H)→ Antigens measuring antibody titers for:- Salmonella Typhi (TO, TH)- Salmonella Paratyphi A/B (AH, BH).3. Typhoid Status:Typhoid→ Overall diagnosis (e.g., Minimal/Negative/Positive).Acute_typhoid (Yes/No)→ Confirms active infection (likely based on clinical symptoms + high titers).Paratyphoid_A/B (Yes/No)→ Specific to Salmonella Paratyphi A or B infections.4. Rickettsial SuspicionRickettsia_Suspect (Yes/No)→ Flagged if Weil-Felix titers (OXK/OX2/OX19) or symptoms suggest rickettsiosis.5. Additional Diagnostic MarkersM, A (Binary: 0/1)→ Likely represent: M: IgM antibody presence (acute phase); A: Agglutination result (if qualitative).6. Demographic & Privacy Fields: Gender (Male/Female); Age (Years); Encrypted_Name: → Deidentified patient records for ethical compliance.Dataset Validation:Validated by: Dr. Prio Gopal Biswas (Medical Doctor)Hospital: Saranjghola Upazila Health Complex, Bagerhat, BangladeshData Collection Period: October 27, 2024 – January 2, 2025Ethical Protocols and Statement: All procedures were conducted in alignment with the ethical guidelines of Daffodil International University (DIU) and in accordance with relevant national and institutional regulations. The Research Ethics Committee (REC) of the Faculty of Science and Information Technology (FSIT) at DIU granted ethical approval for this study under the approval number REC-FSIT-2024-09-17, following a thorough review process. Written informed consent was obtained from all participants.

This dataset contains detailed clinical records of 1,106 patients collected from Bangladesh between October 27, 2024 and January 2, 2025. Typhoid fever is a serious bacterial infection caused by Salmonella Typhi, which is commonly spread through contaminated food and water. It remains a widespread health issue in many parts of Bangladesh, especially where sanitation and clean water access are limited. The dataset includes results from Widal tests, which are commonly used to detect typhoid and paratyphoid fever. These include antigens such as TO and TH (specific to Salmonella Typhi) and AH and BH (specific to Salmonella Paratyphi). Additionally, the dataset contains values from the Weil-Felix test, including OXK, OX2, and OX19, which are used to help identify rickettsial infections — a group of bacterial infections often presenting with similar symptoms. This dataset has been validated by a certified medical professional and is suitable for use in epidemiological research, diagnostic modeling, and public health analysis. 1. Weil-Felix Test (Rickettsial Infections): OXK, OX2, OX19→ Antigens used to detect antibodies against rickettsial infections (e.g., scrub typhus, epidemic typhus).Note: Weil-Felix is nonspecific and largely replaced by serological/PCR tests in modern labs.2. Widal Test (Typhoid/Paratyphoid Fever): TO (Typhi O), TH (Typhi H), AH (Paratyphi A H), BH (Paratyphi B H)→ Antigens measuring antibody titers for:- Salmonella Typhi (TO, TH)- Salmonella Paratyphi A/B (AH, BH).3. Typhoid Status:Typhoid→ Overall diagnosis (e.g., Minimal/Negative/Positive).Acute_typhoid (Yes/No)→ Confirms active infection (likely based on clinical symptoms + high titers).Paratyphoid_A/B (Yes/No)→ Specific to Salmonella Paratyphi A or B infections.4. Rickettsial SuspicionRickettsia_Suspect (Yes/No)→ Flagged if Weil-Felix titers (OXK/OX2/OX19) or symptoms suggest rickettsiosis.5. Additional Diagnostic MarkersM, A (Binary: 0/1)→ Likely represent: M: IgM antibody presence (acute phase); A: Agglutination result (if qualitative).6. Demographic & Privacy Fields: Gender (Male/Female); Age (Years); Encrypted_Name: → Deidentified patient records for ethical compliance.Dataset Validation:Validated by: Dr. Prio Gopal Biswas (Medical Doctor)Hospital: Saranjghola Upazila Health Complex, Bagerhat, BangladeshData Collection Period: October 27, 2024 – January 2, 2025Ethical Protocols and Statement: All procedures were conducted in alignment with the ethical guidelines of Daffodil International University (DIU) and in accordance with relevant national and institutional regulations. The Research Ethics Committee (REC) of the Faculty of Science and Information Technology (FSIT) at DIU granted ethical approval for this study under the approval number REC-FSIT-2024-09-17, following a thorough review process. Written informed consent was obtained from all participants.

The Frangipani Leaf Image Dataset is a curated and structured collection of images aimed at advancing research in computer vision, machine learning, and deep learning for plant disease identification and classification. This dataset contains high-quality images of Frangipani leaves, both healthy and diseased, to support the development of automated early disease detection systems in agriculture. Dataset StructureOriginal:-NUmber of images:1,789 -Formate:jpeg,.jpg Preprocessed: -NUmber of images:1,789 -Formate:jpeg,.jpg Augmented:-NUmber of images:10,500-Formate:jpeg,.jpgAugmentation Techniques UsedTo increase dataset diversity and enhance model generalization, the following augmentation techniques were applied:Rotation,Flipping,Brightness Adjustment,Contrast Adjustment,Blurring,Shearing,Scaling.Use Case and ImpactEarly Disease Detection: Enables real-time diagnosis through automated models, reducing reliance on manual inspections.Sustainable Agriculture: Facilitates precision treatment and reduces excessive pesticide use.Mobile & IoT Integration: Supports the development of lightweight models for field-deployable applications in smart farming systems.

The Frangipani Leaf Image Dataset is a curated and structured collection of images aimed at advancing research in computer vision, machine learning, and deep learning for plant disease identification and classification. This dataset contains high-quality images of Frangipani leaves, both healthy and diseased, to support the development of automated early disease detection systems in agriculture. Dataset StructureOriginal:-NUmber of images:1,789 -Formate:jpeg,.jpg Preprocessed: -NUmber of images:1,789 -Formate:jpeg,.jpg Augmented:-NUmber of images:10,500-Formate:jpeg,.jpgAugmentation Techniques UsedTo increase dataset diversity and enhance model generalization, the following augmentation techniques were applied:Rotation,Flipping,Brightness Adjustment,Contrast Adjustment,Blurring,Shearing,Scaling.Use Case and ImpactEarly Disease Detection: Enables real-time diagnosis through automated models, reducing reliance on manual inspections.Sustainable Agriculture: Facilitates precision treatment and reduces excessive pesticide use.Mobile & IoT Integration: Supports the development of lightweight models for field-deployable applications in smart farming systems.

The MA-LeafFruitDx (Sonneratia caseolaris) Leaf and Fruit Disease Classification Dataset is a curated image dataset designed to support advanced research in plant health monitoring using machine learning, deep learning, and computer vision. This dataset captures both healthy and diseased states of leaves and fruits of the Mangrove Apple (Sonneratia caseolaris), a plant native to the Sundarban mangrove forest in Bangladesh.Total Number of Images; Original Dataset Size: 2,307 imagesAugmented Dataset Size: 17,500 images (3,500 per class × 5 classes)Class-wise Image Distribution (Original Dataset)1. Healthy Fruits: 4202. Healthy Leaves: 4943. Insect Hole Leaves: 4104. Unhealthy Fruits: 4775. Yellow Leaves: 506Data Augmentation: To address class imbalance and improve model generalization, the dataset was augmented to include 3,500 images per class, bringing the total dataset size to 17,500 images. The following augmentation techniques were applied:- Geometric Transformations: Rotation, flipping (horizontal and vertical), random cropping- Photometric Enhancements: Brightness adjustment, contrast modification, Gaussian blur- Noise Injection: Gaussian noise and salt-and-pepper noise- Color Space Variations: HSV shifts, grayscale conversion- Perspective and Affine TransformationsImage Format: All images are in .jpg format.Camera Devices Used: 1. Samsung Galaxy S23+: 1,5682. OnePlus GM1901: 739Geographical Location: Data Collection Site: Sundarban Mangrove Forest, Bangladesh — a UNESCO World Heritage Site and home to a wide diversity of plant and animal species.🚀 Application AreasThis dataset has diverse applications across ML, DL, and CV research fields:✅ 1. Machine Learning1. Feature engineering and selection for classical ML models (e.g., SVM, Random Forest, XGBoost)2. Disease pattern classification using handcrafted features3. Model benchmarking for low-resource environments✅ 2. Deep Learning1. Training and evaluation of Convolutional Neural Networks (CNNs)2. Transfer learning with pre-trained models (e.g., ResNet, EfficientNet, InceptionV3)3. Fine-grained image classification and object localization4. Use in multi-class classification and semantic segmentation tasks✅ 3. Computer Vision1. Development of real-time plant disease detection systems2. Implementation in mobile applications for on-field crop monitoring3. Use in smart agriculture systems (IoT + CV)4. Integration with image segmentation and disease severity scoring5. Vision transformers (ViTs) and self-supervised learning explorationThis dataset provides a rich, high-quality resource for researchers aiming to advance the state-of-the-art in automated plant disease recognition, especially in mangrove ecosystems, which are often underrepresented in computer vision literature.

The MA-LeafFruitDx (Sonneratia caseolaris) Leaf and Fruit Disease Classification Dataset is a curated image dataset designed to support advanced research in plant health monitoring using machine learning, deep learning, and computer vision. This dataset captures both healthy and diseased states of leaves and fruits of the Mangrove Apple (Sonneratia caseolaris), a plant native to the Sundarban mangrove forest in Bangladesh.Total Number of Images; Original Dataset Size: 2,307 imagesAugmented Dataset Size: 17,500 images (3,500 per class × 5 classes)Class-wise Image Distribution (Original Dataset)1. Healthy Fruits: 4202. Healthy Leaves: 4943. Insect Hole Leaves: 4104. Unhealthy Fruits: 4775. Yellow Leaves: 506Data Augmentation: To address class imbalance and improve model generalization, the dataset was augmented to include 3,500 images per class, bringing the total dataset size to 17,500 images. The following augmentation techniques were applied:- Geometric Transformations: Rotation, flipping (horizontal and vertical), random cropping- Photometric Enhancements: Brightness adjustment, contrast modification, Gaussian blur- Noise Injection: Gaussian noise and salt-and-pepper noise- Color Space Variations: HSV shifts, grayscale conversion- Perspective and Affine TransformationsImage Format: All images are in .jpg format.Camera Devices Used: 1. Samsung Galaxy S23+: 1,5682. OnePlus GM1901: 739Geographical Location: Data Collection Site: Sundarban Mangrove Forest, Bangladesh — a UNESCO World Heritage Site and home to a wide diversity of plant and animal species.🚀 Application AreasThis dataset has diverse applications across ML, DL, and CV research fields:✅ 1. Machine Learning1. Feature engineering and selection for classical ML models (e.g., SVM, Random Forest, XGBoost)2. Disease pattern classification using handcrafted features3. Model benchmarking for low-resource environments✅ 2. Deep Learning1. Training and evaluation of Convolutional Neural Networks (CNNs)2. Transfer learning with pre-trained models (e.g., ResNet, EfficientNet, InceptionV3)3. Fine-grained image classification and object localization4. Use in multi-class classification and semantic segmentation tasks✅ 3. Computer Vision1. Development of real-time plant disease detection systems2. Implementation in mobile applications for on-field crop monitoring3. Use in smart agriculture systems (IoT + CV)4. Integration with image segmentation and disease severity scoring5. Vision transformers (ViTs) and self-supervised learning explorationThis dataset provides a rich, high-quality resource for researchers aiming to advance the state-of-the-art in automated plant disease recognition, especially in mangrove ecosystems, which are often underrepresented in computer vision literature.

Karanda (Carissa carandas), also known as Bengal currant or Christ's thorn, is a tropical fruit-bearing plant valued for its edible fruits and medicinal benefits. This dataset was created to support the detection and classification of diseases affecting Karanda leaves using image-based machine learning. All images were captured outdoors with a smartphone, reflecting real-world conditions and showing both healthy and diseased leaves under various lighting and environments. The goal is to help researchers and developers build AI models that can detect leaf diseases early, improve crop health monitoring, and support sustainable farming practices for Karanda and similar tropical plants.Original Dataset- Number of Images: 715- Data Format: .jpgProcessed Dataset- Number of Images: 715- Data Format: .jpgAugmented Dataset- Number of Images: 7,000- Data Format: .jpgAugmentation Techniques Applied:1. Rotation2. Horizontal Flipping3. Vertical Flipping4. Brightness Enhancement5. Contrast Adjustment6. Image Blurring7. Shearing8. Zooming9. Noise InjectionApplications:- Enables AI-based early detection of Karanda leaf diseases to improve crop yield and reduce economic losses.- Supports the development of automated classification systems for tropical plant species.- Aids research in precision agriculture by enabling machine learning models to assess plant health visually.- Provides a solid foundation for building robust, real-time plant disease detection solutions deployable in field conditions.- Facilitates academic and industrial research on plant pathology using computer vision and deep learning.

Karanda (Carissa carandas), also known as Bengal currant or Christ's thorn, is a tropical fruit-bearing plant valued for its edible fruits and medicinal benefits. This dataset was created to support the detection and classification of diseases affecting Karanda leaves using image-based machine learning. All images were captured outdoors with a smartphone, reflecting real-world conditions and showing both healthy and diseased leaves under various lighting and environments. The goal is to help researchers and developers build AI models that can detect leaf diseases early, improve crop health monitoring, and support sustainable farming practices for Karanda and similar tropical plants.Original Dataset- Number of Images: 715- Data Format: .jpgProcessed Dataset- Number of Images: 715- Data Format: .jpgAugmented Dataset- Number of Images: 7,000- Data Format: .jpgAugmentation Techniques Applied:1. Rotation2. Horizontal Flipping3. Vertical Flipping4. Brightness Enhancement5. Contrast Adjustment6. Image Blurring7. Shearing8. Zooming9. Noise InjectionApplications:- Enables AI-based early detection of Karanda leaf diseases to improve crop yield and reduce economic losses.- Supports the development of automated classification systems for tropical plant species.- Aids research in precision agriculture by enabling machine learning models to assess plant health visually.- Provides a solid foundation for building robust, real-time plant disease detection solutions deployable in field conditions.- Facilitates academic and industrial research on plant pathology using computer vision and deep learning.

This dataset has been created to support the detection and classification of diseases affecting sapodilla leaves using image-based machine learning methods. Images were captured under real-world outdoor conditions with a smartphone, representing a variety of healthy and diseased leaves. The dataset provides a strong foundation for building AI models aimed at plant disease diagnosis, agricultural automation, and precision farming for sapodilla crops.Original Dataset: -Number of images: 1,778-Data format: .jpgProcessed Dataset:-Number of images: 1,778-Data format: .jpgAugmented Dataset:-Number of images: 14,000-Data format: .jpgAugmentation Techniques:1. Rotation, 2. Horizontal Flipping, 3. Vertical Flipping, 4. Brightness Enhancement, 5. Contrast Variation, 6. Image Blurring, 7. Shearing, 8. Zooming, 9. Noise InjectionApplications:-Enables AI-based early detection of sapodilla leaf and fruit diseases to support better farm management.-Useful for creating automated classification systems for tropical fruit crops.-Supports research in precision farming by enabling image-based plant health assessment models.-Provides a robust base for improving disease detection accuracy in real-world agricultural settings.

This dataset has been created to support the detection and classification of diseases affecting sapodilla leaves using image-based machine learning methods. Images were captured under real-world outdoor conditions with a smartphone, representing a variety of healthy and diseased leaves. The dataset provides a strong foundation for building AI models aimed at plant disease diagnosis, agricultural automation, and precision farming for sapodilla crops.Original Dataset: -Number of images: 1,778-Data format: .jpgProcessed Dataset:-Number of images: 1,778-Data format: .jpgAugmented Dataset:-Number of images: 14,000-Data format: .jpgAugmentation Techniques:1. Rotation, 2. Horizontal Flipping, 3. Vertical Flipping, 4. Brightness Enhancement, 5. Contrast Variation, 6. Image Blurring, 7. Shearing, 8. Zooming, 9. Noise InjectionApplications:-Enables AI-based early detection of sapodilla leaf and fruit diseases to support better farm management.-Useful for creating automated classification systems for tropical fruit crops.-Supports research in precision farming by enabling image-based plant health assessment models.-Provides a robust base for improving disease detection accuracy in real-world agricultural settings.