COT-AD: Cotton Analysis Dataset

2025-07-24

AI Summary

The lack of crop-specific agricultural vision datasets hinders the development of intelligent cotton management systems. Method: This study introduces COT-AD, the first multimodal, full-growth-cycle cotton analysis dataset, comprising 25,000 field images captured by UAVs and DSLR cameras, with 5,000 meticulously annotated for tasks including pest/disease classification, vegetation/weed segmentation, and early disease detection. Contribution/Results: COT-AD is the first dataset to enable synchronized, multi-scenario, multi-task annotation across all cotton phenological stages, thereby filling a critical domain gap. It supports training and evaluation of classification, segmentation, and generative models, and empirical validation demonstrates its effectiveness in early disease warning and precision field management. By providing diverse, high-quality, task-aligned annotations, COT-AD significantly enhances the generalizability and practical applicability of agricultural vision models for smart cotton cultivation.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Data transparency and provenance

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

Problem

• Enhance cotton crop analysis via computer vision
• Address lack of cotton-specific agricultural datasets
• Support disease management and crop synthesis

Innovation

• Comprehensive cotton dataset with 25,000 images
• Aerial and DSLR imagery for disease analysis
• Supports classification, segmentation, and generative models