COT-AD: Cotton Analysis Dataset
This addresses a critical gap in agricultural datasets for cotton farmers and researchers, but it is incremental as it applies existing methods to new data.
The paper tackles the lack of cotton-specific agricultural datasets by introducing COT-AD, a comprehensive dataset with over 25,000 images including 5,000 annotated ones, which supports tasks like classification, segmentation, and disease management to enhance cotton crop analysis.
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