Ai-powered remote sensing system for in-season cotton management
An integrated UAS and satellite remote sensing platform using AI tools for in-season management and seasonal forecasting of sustainable cotton production. Combines high-resolution drone data with calibrated satellite imagery to enable precision crop monitoring, stress detection, and input optimization across large fields.
Overview
This solution integrates Unmanned Aerial System (UAS) and satellite-based remote sensing with Artificial Intelligence (AI) tools to develop an in-season management system for sustainable cotton production. The platform addresses the limitations of using either technology alone by combining high spatio-temporal resolution data from drones with the broad coverage of satellite imagery.
The system enables cotton producers to monitor crop growth, detect abiotic and biotic stress, assess nutrient requirements, and make data-driven decisions about input application throughout the growing season. By accounting for spatial variability and crop needs, the technology supports more efficient and sustainable cotton farming practices.
Technical specifications
Data collection and processing pipeline:
- Semi-automated UAS data collection protocols using RGB and multi-spectral cameras
- Orthomosaic generation for extracting canopy features including canopy height and cover
- Vegetation index computation from high-resolution drone imagery
- Satellite image calibration using Artificial Neural Network (ANN) models trained on UAS data
- Data extraction at 1m x 1m grid resolution across large fields
- Growth curve development for phenotypic traits with over 90% accuracy in testing data
AI and modeling components:
- Machine learning algorithms including ANN and Convolutional Neural Networks (CNN)
- Integration of multi-temporal UAS data, satellite data, weather information, and crop models
- Digital twin modeling for cotton growth simulation
- Crop growth and yield forecasting capabilities
- Web-based decision support system for in-season input application recommendations
Technology readiness level
The technology has been validated through multi-year field work with cotton producers at Corpus Christi, TX since 2018, covering a 100-acre cotton farm. The research team has demonstrated the ability to calibrate satellite imagery with greater than 90% accuracy for phenotypic traits across multiple locations.
Future development will focus on building a comprehensive digital twin model that integrates all data sources through AI tools to create a complete crop growth and yield forecasting system. The web-based platform will provide actionable recommendations for input application, stress monitoring, and nutrient management, advancing the technology toward broader commercial deployment for sustainable cotton production.
About Texas A&M University, College Station
Texas A&M University in College Station is a comprehensive public research university and the flagship of The Texas A&M University System, combining broad academic strengths with a strong applied‑research culture. Industry collaborates on the Texas A&M‑RELLIS campus—an integrated education, research and testing environment that supports large‑scale experimentation and proving grounds—and through the Texas A&M Transportation Institute’s facilities in Bryan‑College Station. A statewide extension network connects university expertise to companies and communities across all Texas counties, enabling rapid piloting and deployment. Research is supported by competitive federal funding from agencies such as NSF, NIH, DOE, USDA and DoD, alongside state and industry sponsorship. Texas A&M Innovation provides IP management, licensing and commercialization pathways across the system.
delivered to your inbox.