Skip to main content

Beech leaf disease symptom detection using deep learning and computer vision tools Cover

.blurhash-client-img { display: none !important; }

Beech leaf disease symptom detection using deep learning and computer vision tools

Journal of Nematology

Volume 58 (2026): Issue 1 (February 2026)

By: Benjamin D. Waldo, Paulo Vieira, Matthew A. Borden and Shiguang Li

Open Access

|Apr 2026

Figures & tables

Figures & Tables

Image examples from beech leaf disease (BLD) Dataset I displaying various banding symptoms across multiple environments. (a) BLD symptomatic leaves in the forest imaged with a digital camera. (b) Abaxial surface of the BLD leaf imaged in the laboratory with a digital camera. (c) BLD leaf in the laboratory imaged with a digital camera. (d) BLD leaves detached from a tree outdoors imaged with a digital camera. (e) BLD leaves in the laboratory imaged with a smartphone. (f) BLD leaves imaged on the laboratory bench with a digital camera.

Training and validation accuracy of EfficientNetV2-S, InceptionV3, MobileNetV3-L, and ResNet50 models at distinguishing leaves with beech leaf disease and without beech leaf disease.

Heatmaps of discriminative regions for top-performing model EfficientNetV2-S at detecting beech leaf disease symptoms on Dataset II images. Heatmap generated with Gradient-weighted Class Activation Mapping (Grad-CAM). (a) An image collected in a forest and (b) its corresponding Grad-CAM heat map visualization. (c) A representative image of a detached leaf and (d) the associated Grad-CAM visualization. Red areas are strongly discriminative, and blue areas are weakly discriminative.

Dataset II test evaluation metrics of EfficientNetV2-S, InceptionV3, MobileNetV3-L, and ResNet50 pre-trained convolutional neural network models trained to distinguish leaves symptomatic and asymptomatic for beech leaf disease_

Model	Accuracy	Precision	Recall	F1	AUC–ROC
EfficientNetV2-S	96.55	91.84	100	95.74	99.87
InceptionV3	86.21	93.94	68.89	79.49	96.28
MobileNetV3-L	87.93	84.44	84.44	84.44	93.93
ResNet50	85.34	76.92	88.89	82.47	93.80

Dataset I test evaluation metrics of EfficientNetV2-S, InceptionV3, MobileNetV3-L, and ResNet50 pre-trained convolutional neural network models trained to distinguish leaves symptomatic and asymptomatic for beech leaf disease_

Model	Accuracy	Precision	Recall	F1	AUC–ROC
EfficientNetV2-S	100	100	100	100	100
InceptionV3	94.88	93.33	94.12	93.72	99.17
MobileNetV3-L	97.95	98.29	96.64	97.46	99.88
ResNet50	99.32	100	98.32	99.15	99.99

Articles in this issue

DOI: https://doi.org/10.2478/jofnem-2026-0011 | Journal eISSN: 2640-396X | Journal ISSN: 0022-300X

Journal RSS Feed

Language: English

Page range: 66 - 77

Submitted on: Jan 20, 2026

|

Accepted on: Mar 20, 2026

|

Published on: Apr 27, 2026

Published by: Society of Nematologists, Inc.

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Convolutional neural networks,

Fagus grandifolia,

Forest health monitoring,

Foliar nematode,

Plant disease detection

Related subjects:

Life sciences, other

© 2026 Benjamin D. Waldo, Paulo Vieira, Matthew A. Borden, Shiguang Li, published by Society of Nematologists, Inc.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 58 (2026): Issue 1 (February 2026)

Previous article