Feature extraction approaches for leaf area index estimation in California vineyards via machine learning algorithms

Accurate leaf area index (LAI) estimation through machine learning (ML) algorithms is a channel for better understanding and monitoring the existing biomass and it relates to the distribution of energy fluxes and evapotranspiration partitioning. In order to support the ML algorithm for accurate LAI estimation, the supporting data (or features) gained from the sUAS platform are challenging in terms of variety, quantity, and quality. This project provides two types of feature-extraction approaches and the demo data to show how a variety of features are generated based on the sUAS platform via the python language. This project is also part of our pending paperwork. Other researchers can also use this project based on their sUAS platform to gain the features for estimation of their interested parameters, such as biomass and leaf water potential.