Accurate Identification of Ilex (Aquifoliaceae) Taxa Based on Leaf Morphology Using Deep Learning
Year: 2026
Type: article
Abstract: Holly (Ilex L.) is a genus of woody dioecious plants with substantial ecological and economic value. However, its high species diversity and morphological similarity make accurate identification challenging. To address this, we constructed a multi-taxon Ilex leaf image dataset. We then trained six deep learning models—GoogLeNet, ResNet50, ResNet101, DenseNet121, DenseNet169, and EfficientNet-B3—using a unified PyTorch framework on cloud computing resources. Leaf images were preprocessed by background removal, resizing, cropping, and normalization. Model performance was evaluated using accuracy, F1-score, and Grad-CAM visualizations. Under an image-level data split that may overestimate generalization, all six models achieved over 99% classification accuracy on preprocessed leaf images under controlled laboratory conditions. DenseNet121 and DenseNet169 performed best, reaching 99.65% accuracy. Because images of the same leaf or same plant could appear in both training and test sets under this split, plant-level cross-validation is required to assess real-world generalizability. The reported accuracies represent an upper-bound estimate under image-level splitting. The framework offers a rapid and accurate tool for preliminary screening under controlled conditions, but its performance on raw field photographs and across different collection sites remains to be validated.
Source: Plants
Authors: Lin Yang, Yizhe Zhao, Cheng Jin, Shichang Wu, Zeyu Lu, Mingzhuo Hao, Changwei Bi, Kewang Xu
Institution: Nanjing Forestry University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7160068541
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