AI models estimate cotton growth using UAV data

Researchers have developed advanced machine learning models, specifically Random Forest Regression (RFR) and Extreme Gradient Boosting (XGB), to estimate critical cotton growth parameters. These models integrate spectral and morphological plant features, such as plant height and fractional canopy cover, derived from unmanned aerial vehicle (UAV) data. The study, conducted over three years in the Texas Coastal Plains, demonstrated high accuracy in estimating dry biomass weight, plant nitrogen uptake, and plant nitrogen concentration, supporting precision nitrogen management in cotton farming. AI

IMPACT Enhances precision agriculture by enabling early-season monitoring of crop health and nutrient status, potentially optimizing fertilizer use and yield.

RANK_REASON This is a research paper detailing a new methodology for agricultural monitoring using machine learning and UAV data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Cotton
• Texas Coastal Plains
• unmanned aerial vehicle
• Vaishali Swaminathan
• XGBoost

• paper
• product
• other

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