The identification of defects in apple leaf specimens is crucial for mitigating crop loss and maintaining harvest quality. This study investigates the applicability of an intensity detection simulation using an integrated optical cross-sectional modeling method for detecting defective apple leaf specimens. The technique utilizes customized 840 nm optical coherence tomography (OCT) as the imaging tool, visualizing sufficient depth with a micrometer resolution. Leaf specimens were collected from apple plantations in Korea and categorized as healthy, apparently healthy, and infected leaf specimens. The method involved using a peak-intensity detection technique to analyze OCT signal intensity variations in multi-layered leaf structures. The method enhances defect detection accuracy by precisely characterizing the optical properties of the leaf specimens. The results demonstrate the method's potential to identify morphological differences between leaf specimens from healthy and infected trees and, specifically, healthy leaf specimens from infected trees. Through the quantitative analysis of OCT images, including quantitative information on cross-sectional thickness and depth direction, the method provides valuable insights into the structural changes associated with leaf defects, such as discoloration, tissue degradation, and altered layer morphology. Implementing this method in apple orchards can lead to significant cost savings by enabling timely interventions to mitigate the impact of leaf defects on crop production.