Abstract
Accurate differentiation of visually similar cherry leaf diseases remains a major challenge in precision agriculture due to overlapping symptom patterns and environmental variability. This study presents a comprehensive deep learning–based framework for multi-class cherry leaf disease classification, integrating systematic architectural comparison, statistical validation, and explainable artificial intelligence (XAI) analysis. Contrary to the common assumption that increased architectural complexity enhances performance, our results show that dual-backbone architectures consistently fail to outperform single-backbone models. A dataset comprising 4,995 cherry leaf images across five categories—brown spot, leaf scorch, healthy leaf, purple leaf spot, and shot hole disease—was used to evaluate multiple convolutional neural network architectures under fully standardized conditions. ResNet50 achieved the highest classification accuracy (98.20%), followed by EfficientNetB2 (98.00%) and DenseNet121 (97.50%), while the best dual-backbone model reached only 97.30% despite increased complexity. Statistical analysis using the Wilcoxon signed-rank test revealed a significant discrepancy between overall accuracy and macro-averaged recall (p = 0.00195, r = 0.89), demonstrating that accuracy systematically overestimates class-wise detection performance in multi-class scenarios. Grad-CAM–based explainability analysis further revealed that DenseNet-based models produce compact and semantically coherent activation maps aligned with disease-relevant regions, whereas dual-backbone architectures exhibit fragmented attention patterns associated with feature redundancy and gradient interference. These findings indicate that interpretability fidelity does not scale with architectural complexity and that coherent single-backbone feature hierarchies provide a superior balance between performance, interpretability, and generalization. The proposed framework offers both methodological and practical insights for developing reliable and scalable artificial intelligence systems in agricultural disease diagnostics.
Similar content being viewed by others
Data availability
The image dataset used in this study was obtained from a publicly available secondary source hosted on the Kaggle platform. Specifically, cherry leaf images were accessed from the Cherry Leaf Disease on PlantCity Dataset (2025), which is derived from the PlantCity image collection. The dataset is openly available at: [https://www.kaggle.com/datasets/codewithsk/cherry-leaf-disease-on-plantcity-2025]All images were used in accordance with Kaggle’s open data usage policy and were subsequently curated, preprocessed, and partitioned into training, validation, and testing subsets for model development and evaluation. No new data were generated during the current study.
References
Ferentinos, K. P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018).
Bacelar, E. et al. Impacts of climate change and mitigation strategies for some abiotic and biotic constraints influencing fruit growth and quality. Plants 13, 1942 (2024).
Sharma, S. & Chauhan, O. in Fruits and Vegetables Technologies: Postharvest Processing and Packaging 103–173 (Springer, 2025).
Seethapathy, P., Gothandaraman, R., Gurudevan, T. & Malik, I. A. in Handbook of plum fruit 133–176 (CRC, 2022).
Mohanty, S. P., Hughes, D. P. & Salathé, M. Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 215232 (2016).
Zainab, Z. & Mahum, R. Plant disease detection using deep learning techniques. ICCK J. Image Anal. Process. 1, 36–44 (2025).
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. big data. 6, 1–48 (2019).
Pacal, I. et al. A systematic review of deep learning techniques for plant diseases. Artif. Intell. Rev. 57, 304 (2024).
Upadhyay, A. et al. Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artif. Intell. Rev. 58, 92 (2025).
Li, C. et al. An advancing GCT-Inception-ResNet-V3 model for arboreal pest identification. Agronomy 14, 864 (2024).
Kaya, Y. & Gürsoy, E. A review of deep learning architectures for plant disease detection. Turkish J. Biology. 49, 459–497 (2025).
Radhakrishnan, M., Monish, N., Dev, P. S., Kesavan, N. & Thomas, N. S. Implementation of explainable Ai in deep learning methods for multiclass classification of plant diseases in mango lLeaves. ELCVIA Electron. Lett. Comput. Vis. image Anal. 24, 104–117 (2025).
Srinivasan, S. et al. DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI. BMC Plant Biol. 25, 965 (2025).
Goyal, A. & Lakhwani, K. Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases. Sci. Rep. 15, 12659 (2025).
Van Zyl, C., Ye, X. & Naidoo, R. Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP. Appl. Energy. 353, 122079 (2024).
Ercan, U. et al. Identification and Classification of Snack-Type Watermelon (Citrullus lanatus) Genotypes Using Seed Morphology and Machine Learning Techniques. Foods 14, 4069 (2025).
Naveen, K. & Parvathi, R. AI powered multi feature fusion framework for retrieving images using color, texture and shape descriptors. Scientific Reports (2025).
Khan, M. S., Nisa, K., Ahmad, I., Zubair, M. & Alshammari, K. PlantCity: A Comprehensive Image Based on Multi Crop Leaves in Pakistan. Data Brief, 112130 https://www.kaggle.com/datasets/codewithsk/cherry-leaf-disease-on-plantcity-2025 (2025). (accessed on 10 January 2026).
Khomkham, B. & Pankaseam, Y. Lightweight Convolutional Neural Network Model Based on Modified MobileNetV3 for Plant Disease Classification. SN Comput. Sci. 6, 1028 (2025).
Zhao, Z., Bakar, E. B. A. & Razak, N. B. A. & Akhtar, M. N. Corrosion image classification method based on EfficientNetV2. Heliyon 10 (2024).
Nandhini, S. & Ashokkumar, K. An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm. Neural Comput. Appl. 34, 5513–5534 (2022).
Sumon, R. I. et al. A Deep Learning-Based Approach for Precise Emotion Recognition in Domestic Animals Using EfficientNetB5 Architecture. Eng 6, 9 (2025).
Peng, C., Liu, Y., Yuan, X. & Chen, Q. Research of image recognition method based on enhanced inception-ResNet-V2. Multimedia Tools Appl. 81, 34345–34365 (2022).
Ahmed, S. F. et al. Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif. Intell. Rev. 56, 13521–13617 (2023).
Taye, M. M. Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation 11, 52 (2023).
Rajanand, A. & Singh, P. ErfReLU: adaptive activation function for deep neural network. Pattern Anal. Appl. 27, 68 (2024).
Jabir, B. & Falih, N. Dropout, a basic and effective regularization method for a deep learning model: A case study. Indonesian J. Electr. Eng. Comput. Sci. 24, 1009–1016 (2021).
Kayaalp, K. A deep ensemble learning method for cherry classification. Eur. Food Res. Technol. 250, 1513–1528 (2024).
Reyad, M., Sarhan, A. M. & Arafa, M. A modified Adam algorithm for deep neural network optimization. Neural Comput. Appl. 35, 17095–17112 (2023).
Anam, M. K., Defit, S., Haviluddin, H., Efrizoni, L. & Firdaus, M. B. Early stopping on CNN-LSTM development to improve classification performance. J. Appl. Data Sci. 5, 1175–1188 (2024).
Torghabeh, F. A., Modaresnia, Y. & Khalilzadeh, M. M. Effectiveness of learning rate in dementia severity prediction using VGG16. Biomedical Engineering: Appl. Basis Commun. 35, 2350006 (2023).
Dudala, J. M. in 2024 International Conference on Decision Aid Sciences and Applications (DASA). 1–7 (IEEE).
Sevindik, M. et al. A Comparative Analysis of CNN Architectures, Fusion Strategies, and Explainable AI for Fine-Grained Macrofungi Classification. Biology 14, 1733 (2025).
Korkmaz, A. F. et al. Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi. Biology 14, 1644 (2025).
Kumru, E. et al. Explainable convolutional neural network architectures for high-performance taxonomic classification of gasteroid macrofungi. Sci. Rep. 15, 40196 (2025).
Kumru, E. et al. Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species. Biology 14, 1313 (2025).
Yang, Y., Khorshidi, H. A. & Aickelin, U. A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems. Front. Digit. health. 6, 1430245 (2024).
Jelni, O. S., Radhitya, M. L., Wardhana, G. W., Kusuma, N. W. J. & Desmayani, N. M. M. R. Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN. Indonesian J. Data Sci. 6, 548–562 (2025).
Saheed, Y. K., Balogun, B. F., Odunayo, B. J. & Abdulsalam, M. Microarray gene expression data classification via Wilcoxon sign rank sum and novel Grey Wolf optimized ensemble learning models. IEEE/ACM Trans. Comput. Biol. Bioinf. 20, 3575–3587 (2023).
Wilcoxon, F. Individual comparisons by ranking methods. Biometrics Bull. 1, 80–83 (1945).
Wilcoxon, F. Probability tables for individual comparisons by ranking methods. Biometrics 3, 119–122 (1947).
Lovakov, A. & Agadullina, E. R. Empirically derived guidelines for effect size interpretation in social psychology. Eur. J. Social Psychol. 51, 485–504 (2021).
Kansal, K., Chandra, T. B. & Singh, A. ResNet-50 vs. EfficientNet-B0: multi-centric classification of various lung abnormalities using deep learning. Procedia Comput. Sci. 235, 70–80 (2024).
Debnath, A. et al. A smartphone-based detection system for tomato leaf disease using efficientNetV2B2 and its explainability with artificial intelligence (AI). Sensors 23, 8685 (2023).
Simangunsong, P. B. N., Sihombing, P., Efendi, S. & Fahmi, F. in 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 907–911 (IEEE).
Hemavarshini, S. & Arun, R. A. in 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). 18–22 (IEEE).
Salam, A. et al. Mulberry leaf disease detection using CNN-based smart android application. IEEE Access. 12, 83575–83588 (2024).
Naskinova, I. Transfer learning with NASNet-Mobile for Pneumonia X-ray classification. Asian-European J. Math. 16, 2250240 (2023).
Fauzi, D. R. Comparison of CNN Models Using EfficientNetB0, MobileNetV2, and ResNet50 for Traffic Density with Transfer Learning. J. Intell. Syst. Technol. Inf. 1, 22–30 (2025).
Jesupriya, J., Mageswari, P. U. & Alli, A. in 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). 1–9 (IEEE).
Tripathi, A., Singh, T., Nair, R. R. & Duraisamy, P. Improving early detection and classification of lung diseases with innovative MobileNetV2 framework. IEEE Access (2024).
Mutasodirin, M. A. & Falakh, F. M. Efficient weather classification using densenet and efficientnet. Jurnal Informatika: Jurnal Pengembangan IT. 9, 173–179 (2024).
An, Q., Chen, W. & Shao, W. A deep convolutional neural network for pneumonia detection in X-ray images with attention ensemble. Diagnostics 14, 390 (2024).
Riska, S. Y., Sulistyo, D. A. & Maharani, F. S. S. High-accuracy classification of banana varieties using ResNet-50 and DenseNet-121 architectures. Indonesian J. Electr. Eng. Comput. Sci. 39, 322–335 (2025).
Ramzan, M., Bustaman, A., Sarwinda, D. & Ullah, N. in 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 1089–1094 (IEEE).
Feng, S., Li, Z., Zhang, B., Chen, T. & Wang, B. DSF2-NAS: Dual-Stage Feature Fusion via Network Architecture Search for Classification of Multimodal Remote Sensing Images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing (2025).
Ennab, M. & Mcheick, H. Advancing AI interpretability in medical imaging: a comparative analysis of pixel-level interpretability and Grad-CAM models. Mach. Learn. Knowl. Extr. 7, 12 (2025).
Yu, X., Liu, M., Yu, S., Shi, X. & Wang, Q. The Dense Convolutional Network for Rice Disease Recognition Based on Transfer Learning. Curr. Sci. 5, 3595–3606 (2025).
Wang, R. et al. MarsTerrNet: A U-Shaped Dual-Backbone Framework with Feature-Guided Loss for Martian Terrain Segmentation. Remote Sens. 18, 35 (2025).
Reddy, N. L. & Gopinath, M. Advanced deep learning framework for soil texture classification. Sci. Rep. 15, 34407 (2025).
Kadir, M. A., Mosavi, A. & Sonntag, D. in IEEE 27th International Conference on Intelligent Engineering Systems (INES). 000111–000124 (IEEE). 000111–000124 (IEEE). (2023).
Kainat, J. et al. Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques. Complexity 9736179 (2021). (2021).
Kondaveeti, H. K. & Simhadri, C. G. Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection. Sci. Rep. 15, 31850 (2025).
Georgiou, T., Liu, Y., Chen, W. & Lew, M. A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision. Int. J. Multimedia Inform. Retr. 9, 135–170 (2020).
Sahoo, L. et al. Transforming agriculture through artificial intelligence: Advancements in plant disease detection, applications, and challenges. J. Adv. Biology Biotechnol. 27, 381–388 (2024).
Manoj, H. et al. AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas. Scientific Reports (2025).
Acknowledgements
None.
Ethics declaration: Not applicable.
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
Conceptualization, H.T.A., Ö.D., F.E. K.A., M.S., and E.K.; Methodology, M.S.G. and I.A.; Software, Ö.D., K.A., and T.A.; Validation, M.S.G., I.A. K.A., M.S., and F.E.; Formal Analysis, Ö.D., H.T.A. K.A., M.S., and E.K.; Investigation, Ö.D., F.E. and K.A.; Resources, Ö.D., M.S.G., F.E., K.A., I.A and T.A.; Writing – Original Draft Preparation, Ö.D., H.T.A., I.A. and F.E.; Writing – Review & Editing, M.S.G., M.S.,; Visualization, H.T.A., and F.E.; Supervision, M.S.G, I.A. and F.E.
Corresponding author
Ethics declarations
Competing Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Altay, H.T., Demir, Ö., Ekinci, F. et al. A comparative analysis of single- and dual-backbone deep learning architectures with explainable AI for cherry leaf disease classification. Sci Rep (2026). https://doi.org/10.1038/s41598-026-50104-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-50104-1
