Artificial intelligence-based modeling for accurate leaf area estimation in olive (Olea europaea L.) cultivars.

ABSTRACT: Estimating olive (Olea europaea L.) leaf area is an important aspect of monitoring plant health and evaluating growth processes in agriculture. Accurate estimation of leaf area allows for a better understanding of processes such as water and nutrient utilization, photosynthesis efficiency, respiration, and yield potential. This study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves. Additionally, the predictive performances of multiple linear regression (MLR) and artificial neural network (ANN) were compared. A total of 1320 leaf samples collected from 22 olive cultivars were used in the study. Leaf length and width were taken as input parameters, and both MLR and ANN models were developed f

SUBMITTER: Tunc Y 

PROVIDER: S-EPMC12758791 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature