Mango Leaf Disease Images for Hybrid AI Classification

A collection of 4,000 mango leaf images, split into training and test sets, used to evaluate a hybrid ResNet50 and quantum machine learning model. The dataset contains eight disease categories: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Healthy, Powdery Mildew, and Sooty Mould. It was authored by Korandla Vigneshwar Reddy and last updated in June 2026.

Use Cases

• Benchmarking image classification models based on the eight disease categories.
• Training hybrid deep learning and quantum machine learning frameworks based on complex leaf image patterns.
• Comparing the performance of standalone CNNs, classical SVMs, and quantum SVMs based on the described experimental configurations.

Strengths

• Contains 4,000 images across eight distinct disease categories.
• Dataset was split via stratified sampling (80/20) to preserve class distribution.
• The hybrid ResNet50-QSVM model achieved a test accuracy of 0.989 ± 0.005, as reported in the description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset file is a 14.9 KB DOCX document, suggesting the primary data may be hosted elsewhere or is of limited scope.

Provenance

Source

figshare

Collection Method

Likely collected for research on automated mango leaf disease classification.

Freshness

Last updated 2026-06-01 05:24:19; freshness should be verified.