COT-AD: COTTON ANALYSIS DATASET

Akbar Ali1, Mahek Vyas2, Soumyaratna Debnath1, Chanda Grover Kamra3,Jaidev Sanjay Khalane1,Reuben Shibu Devanesan1,Indra Deep Mastan4,Subramanian Sankaranarayanan1,Pankaj Khanna1,Shanmuganathan Raman1

ICIP 2025

1 Indian Institute Of Technology Gandhinagar, India,     2 Larsen & Toubro Technology Services Limited, India,    3 Ashoka University - Sonipat,India,     4 Indian Institute of Technology (BHU) Varanasi, India.
Paper        Supplementary        COT-AD Dataset(IEEE Dataport)    COT-AD Dataset(Kaggle)

Abstract

This paper presents COT-AD, a comprehensive Dataset designed to enhance cotton crop analysis through computer vision. Comprising over 25,000 images captured throughout the cotton growth cycle, with 5,000 annotated images, COT-AD includes aerial imagery for field-scale detection and segmentation and high-resolution DSLR images documenting key diseases. The annotations cover pest and disease recognition, vegetation, and weed analysis, addressing a critical gap in cotton-specific agricultural datasets. COT-AD supports tasks such as classification, segmentation, image restoration, enhancement, deep generative model-based cotton crop synthesis and early disease management, advancing data-driven crop management.

Field Views and Data Examples

Field and Drone Captured Images
(a) showcases the field view of Site 1; (b) and (c) are examples of drone-captured images; (d) showcases the field view of Site 2; and Parts (e), (f), and (g) are examples of DSLR images.

Results : Image enhancement and Sementic segmentation

Image enhancement
Sementic segmentation
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About COT-AD Dataset

The COT-AD dataset supports research in agricultural technology, especially for cotton crop monitoring, disease detection, Computer Vision, Image Processing, and precision farming. It consists of two main sections: Annotated Data and Unannotated Data, each tailored for different crop analysis and model development needs.

Annotated Data

Aerial Images for Detection and Segmentation
High-Resolution DSLR Images for Cotton Crop Insights

Aerial Images for Detection and Segmentation

This section includes images collected over six months, divided into four parts:

  • Part A: First two months
  • Part B: Third month
  • Part C: Fourth month
  • Part D: Fifth and sixth months

Each part contains four subfolders:

  • Images: Aerial JPG images of cotton crops
  • Detection Labels: YOLO-formatted .txt files (single-class detection)
  • Segmentation Masks: Binary JPG masks for segmentation
  • Segmentation Labels: YOLO-formatted .txt files for segmentation

High-Resolution DSLR Images for Cotton Crop Insights

This section focuses on different aspects of cotton crop health:

  • Leaf Disease:
    • Yellowish Leaf
    • Leaf Spot Bacterial Blight
    • Leaf Reddening
    • Fresh Leaf
  • Cotton Boll:
    • Boll Rot
    • Damaged Cotton Boll
    • Healthy Cotton Boll
  • Bugs:
    • Mealy Bug
    • Red Cotton Bug

Unannotated Data

DSLR Data (Week-wise)
Farm_2 (Biweekly Image Acquisition)
Farm_1 (Weekly Image Acquisition)

DSLR Data (Week-wise)

High-resolution close-up images of cotton crops, capturing detailed views of leaves, bolls, and bugs. Includes videos for temporal analysis. Ideal for disease detection, pest monitoring, and growth stage identification.

Farm_2 (Biweekly Image Acquisition)

Drone images captured biweekly at three altitudes (10m, 15m, and 115m):

  • 10m and 15m: Provide detailed crop views
  • 115m: Offers a wide field overview

Enables research on plant development, disease detection, and environmental monitoring across different growth stages.

Farm_1 (Weekly Image Acquisition)

Weekly drone images at altitudes of 10m, 15m, and 115m. Facilitates time-series analysis, crop growth tracking, and anomaly detection through consistent, high-frequency monitoring.

Explore the Dataset

Explore the dataset and contribute to the future of agricultural technology!

Citations


            coming soon 

        
           @misc{ali2025cotad,
            title={COT-AD: Cotton Analysis Dataset},
            author={Akbar Ali and Mahek Vyas and Soumyaratna Debnath and Chanda Grover Kamra and Jaidev Sanjay Khalane and Reuben Shibu Devanesan and Indra Deep Mastan and Subramanian Sankaranarayanan and Pankaj Khanna and Shanmuganathan Raman},
            year={2025},
            eprint={2507.18532},
            archivePrefix={arXiv},
            primaryClass={cs.CV}
            url = {http://arxiv.org/abs/2507.18532},
                                 }