Illegal logging and timber trade continue to pose significant challenges in the Philippines, where accurate wood species identification is essential for enforcement but limited by the need for specialised equipment and expertise. This study aims to evaluate whether AI models for macroscopic wood identification can be developed and deployed by wood scientists without programming expertise using the Xylorix platform, focusing on five Philippine hardwood species: Mangium (Acacia mangium Willd.), Rain Tree [Samanea saman (Jacq.) Merr.], Banuyo (Wallaceodendron celebicum Koord.), Tindalo [Afzelia rhomboidea (Blanco) Vidal], and Ipil [Intsia bijuga (Colebr.) O. Kuntze]. Binary classifiers were trained on 10,663 verified cross-section images from 260 specimens and evaluated using specimen-level mean scoring to mirror operational field conditions. Area Under the ROC Curve (AUC) values ranged from 0.969 (Ipil) to 1.000 (Mangium), and Average Precision (AP) values ranged from 0.589 (Samanea) to 1.000 (Mangium). Four of five species achieved AA grade (AUC and AP both \geq 0.90); Rain Tree received AE (AUC \geq 0.90, AP < 0.60) due to AP compression from its small positive test set (3 specimens). All five classifiers rank their target specimens above non-target specimens with near-perfect fidelity. Specimen-level error analysis revealed 9 false negatives from Ipil, primarily stemming from localized image artifacts and 3 false positives for Rain Tree and 1 false positive for Tindalo caused by shared tribal-level anatomical traits. These findings demonstrate that Xylorix non-programmers can leverage the Xylorix platform to construct operationally reliable wood identification models suitable for field deployment at supply chain checkpoints.

翻译：非法采伐和木材贸易持续对菲律宾构成重大挑战，准确的木材树种鉴定对于执法至关重要，但受限于专业设备和专家知识的匮乏。本研究旨在评估木材科学家能否在无编程经验的情况下，利用Xylorix平台开发和部署针对宏观木材鉴定的AI模型，聚焦五种菲律宾硬木树种：Mangium（Acacia mangium Willd.）、Rain Tree [Samanea saman (Jacq.) Merr.]、Banuyo（Wallaceodendron celebicum Koord.）、Tindalo [Afzelia rhomboidea (Blanco) Vidal]和Ipil [Intsia bijuga (Colebr.) O. Kuntze]。基于来自260份标本的10,663张已验证横截面图像，训练了二分类器，并通过标本级均值评分进行评估以模拟实际现场操作条件。ROC曲线下面积（AUC）值范围为0.969（Ipil）至1.000（Mangium），平均精度（AP）值范围为0.589（Samanea）至1.000（Mangium）。五种树种中有四种达到AA级（AUC和AP均≥0.90）；Rain Tree因阳性测试集样本量较小（3份标本）导致AP压缩，获得AE级（AUC≥0.90，AP<0.60）。所有五个分类器均以接近完美的准确度将目标标本排序于非目标标本之上。标本级错误分析显示：Ipil出现9例假阴性，主要源于局部图像伪影；Rain Tree出现3例假阳性，Tindalo出现1例假阳性，均由共享的族级解剖特征引起。这些发现证明，非编程人员可借助Xylorix平台构建适用于供应链检查站现场部署的、操作可靠的木材鉴定模型。