LeafArea Package: A Tool for Estimating Leaf Area in Andean Fruit Species

Abstract

The LeafArea package is an innovative tool for estimating leaf area in six Andean fruit species, utilizing leaf length and width along with species type for accurate predictions. This research highlights the package’s integration of advanced machine learning algorithms, including GLM, GLMM, Random Forest, and XGBoost, which excels in predictive accuracy. XGBoost’s superior performance is evident in its low prediction errors and high R2 value, showcasing the effectiveness of machine learning in leaf area estimation. The LeafArea package, thus, offers significant contributions to the study of plant growth dynamics, providing researchers with a robust and precise tool for informed decision making in resource allocation and crop management.

Keywords

• precision agriculture
• crop breeding
• high-throughput phenotyping
• modeling techniques