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
@article{arxiv.2507.18532,
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},
journal= {arXiv preprint arXiv:2507.18532},
year = {2025}
}
Comments
Dataset publicly available at: https://ieee-dataport.org/documents/cot-adcotton-analysis-dataset. Accepted to IEEE International Conference on Image Processing (ICIP) 2025