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A benchmark dataset for primitive Indian paddy field images with deep learning based classification.

Prabira Kumar Sethy1, Ranjith Pamerelli2, Santi Kumari Behera3

  • 1Department of Electronics and Communication Engineering, SUIIT, Sambalpur University, Burla, Odisha, India.

Data in Brief
|March 26, 2026
PubMed
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A new dataset of Indian paddy field images aids automated rice variety identification. Deep learning models achieved high accuracy, supporting precision agriculture and crop monitoring research.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Automated crop varietal identification is crucial for precision agriculture.
  • Existing datasets may not adequately represent diverse primitive rice varieties.
  • Developing robust identification systems requires comprehensive benchmark datasets.

Purpose of the Study:

  • To introduce a novel benchmark dataset of primitive Indian paddy field images.
  • To establish a deep learning-based classification baseline for automated variety identification.
  • To facilitate research in crop monitoring and precision agriculture.

Main Methods:

  • A dataset comprising 3400 images of 33 primitive rice varieties was created.
  • Images were captured from diverse field locations in Odisha, India.
Keywords:
Deep feature classificationNASNet-LargePaddy field imagesPrecision agriculturePrimitive rice varieties

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  • A NASNet-Large model was used for feature extraction, followed by an Error Correcting Output Codes (ECOC) classifier.

    • Main Results:

    • The deep learning model achieved high performance metrics, including 0.9424 accuracy and 0.9439 F1-score.
    • High specificity (0.9982) and precision (0.9536) were observed.
    • The classification pipeline demonstrated robust performance on the benchmark dataset.

    Conclusions:

    • The introduced dataset and baseline model provide a valuable resource for rice varietal identification research.
    • This work contributes to advancing automated crop monitoring and precision agriculture techniques.
    • The findings encourage further development of AI-driven solutions in agriculture.