Е-Публикации
Технически университет - София

Abstract: 2025.Plant disease detection plays a key role in modern agriculture, with significant implications for yield management and crop quality. This paper is a continuation of previous research by the authors’ team related to the detection of pathologies on apple tree leaves. In order to eliminate the problem of overfitting in the traditional convolutional neural networks (CNNs) transfer learning layers are added to a residual neural network architecture ResNet50. The suggested model is based on pre-trained CNN whose weight coefficients are adapted until ResNet obtains the final classification. The model implementation uses TensorFlow and Keras frameworks and is developed in Jupyter Notebook environment. In addition, ImageDataGenerator is utilized for data augmentation and preprocessing to increase the classification accuracy of the proposed model. The model is trained using a dataset of 1821 high-resolution apple leaves images divided into four distinct classes: healthy, multiple diseases, rust, and scab. The experimental results demonstrate the effectiveness of the suggested ResNet architecture that outperforms other state-of-the-art deep learning architectures in eliminating the overfitting problem. Identifying different apple leaves pathologies with the proposed model contributes to developing smart agricultural practices.

References

1. Savary S et al (2019) The global burden of pathogens and pests on major food crops. Nat Ecol & Evol 3:430–439
2. Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning—a review. IEEE Access 9:56683–56698
3. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
4. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings conference on computer vision and pattern recognition (CVPR). pp 770–778
5. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1–10
6. Matov P, Filipova-Petrakieva S, Lazarova M, Taralova I (2024) Apple trees leaves pathologies detection using deep learning convolutional neural network. In: Proceedings of international conference on omni-layer intelligent systems (COINS). London, UK, pp 270–273
7. Matov P, Filipova-Petrakieva S, Lazarova M, Taralova I (2024) Hybrid machine learning and deep learning CNN models for apple trees leaves pathologies forecast. In: 12th international scientific conference on computer science, COMSCI’2024. Sozopol, Bulgaria, in press
8. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
9. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
10. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of international conference on machine learning (ICML). pp 6105–6114
11. Tajbakhsh N et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
12. Kaya A, Keceli AS, Catal C, Yalic HY, Temucin H, Tekinerdogan B (2019) Analysis of transfer learning for deep neural network based plant classification models. Comput Electron Agric 158:20–29
13. Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://arxiv.org/abs/1603.04467
14. Nazki H et al (2021) Deep learning based plant disease detection for smart agriculture. Comput Biol Med 133:104481
15. Zhang W et al (2020) Deep convolutional neural networks for plant disease detection. Comput Electron Agric 178:105808
16. Liu P et al (2021) A deep learning framework for plant disease detection using convolutional neural networks. Neural Comput Appl 33:1–16
17. Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosys Eng 180:96–107

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