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        <datestamp>2026-09-14T04:25:02Z</datestamp>
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          <dc:title>Field Rice Disease and Pest Dataset</dc:title>
          <dc:creator>Hairong Zhu (24867217)</dc:creator>
          <dc:subject>Image processing</dc:subject>
          <dc:subject>Crop and pasture protection (incl. pests, diseases and weeds)</dc:subject>
          <dc:subject>Agricultural engineering</dc:subject>
          <dc:subject>Rice disease and pest detectionEdge computing; Lightweight object detection; Neural processing unit; Precision agriculture; Model robustness</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This dataset contains 9,296 high-resolution images of rice diseases and pests acquired under unconstrained field conditions. It was specifically curated to train and evaluate computer vision models (such as YOLO and State-Space Models) in complex agricultural environments involving varied illumination, severe occlusion, and visually ambiguous lesions.Dataset Statistics &amp; Split:Total Images: 9,296Training Set: 7,436 images (acquired from 96 independent capture sessions)Validation Set: 1,860 images (acquired from 24 independent capture sessions)Note: The dataset was rigorously partitioned at the "capture-session" level to prevent temporal and spatial data leakage between the training and validation subsets.Categories (6 Classes): 0: Rice Leaf Roller 1: Rice Water Weevil 2: Rice Leafhopper 3: Bacterial Blight 4: Brown Spot 5: Rice BlastAnnotation Format: The annotations are provided in standard YOLO flat-text format (.txt). Each text file contains bounding box coordinates normalized as:Mandatory Citation Policy: If you use this dataset in your academic research, industrial applications, or publications, you MUST cite our foundational manuscript. Please cite the following preprint (or its subsequent peer-reviewed journal version):Zhu, H., Luo, X., Zeng, Y., Tan, W., Zeng, Q., &amp; Qian, H. (2025). Lightweight State-Space YOLO for Field Rice Disease and Pest Detection: Statistical Evaluation, Robustness Analysis, and Edge-NPU Deployment. Research Square. DOI: https://doi.org/10.21203/rs.3.rs-9928737/v1&lt;/p&gt;</dc:description>
          <dc:date>2026-09-14T04:25:02Z</dc:date>
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