{"ID":2890976,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18532","arxiv_id":"2507.18532","title":"COT-AD: Cotton Analysis Dataset","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","short_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...","url_abs":"https://arxiv.org/abs/2507.18532","url_pdf":"https://arxiv.org/pdf/2507.18532v1","authors":"[\"Akbar Ali\",\"Mahek Vyas\",\"Soumyaratna Debnath\",\"Chanda Grover Kamra\",\"Jaidev Sanjay Khalane\",\"Reuben Shibu Devanesan\",\"Indra Deep Mastan\",\"Subramanian Sankaranarayanan\",\"Pankaj Khanna\",\"Shanmuganathan Raman\"]","published":"2025-07-24T15:58:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}