Optimizing Deep Learning Model Ensembles for Plant Disease Detection through Ablation and Correlation Analysis
Abstract
Early detection of plant diseases is crucial for global food security. While Deep Learning ensemble techniques are widely adopted to improve performance, the assumption that simply aggregating models is always beneficial should be nuanced. This paper addresses this issue by conducting a rigorous analysis of an ensemble of four state-of-the-art architectures (Swin Transformer, Vision Transformer, EfficientNetV2, ConvNeXt) on the PlantDoc dataset, a benchmark known for its complexity.Our approach is twofold. First, we conduct a systematic ablation study to assess how each model contributes to the ensemble's performance. This analysis leads to the counter-intuitive finding that an optimized three-model subset (Swin, ViT, and EfficientNetV2) outperforms the full four-model ensemble. Quantitatively, the pruned ensemble achieves a Macro F1-score of 0.7503 and an accuracy of 0.7619, compared to 0.7409 and 0.7500 for the full set, respectively. Second, to explain this phenomenon, we perform a prediction correlation analysis. This reveals significant predictive redundancy, stemming from the architectural similarities between ConvNeXt and the Transformer-based models, as the cause for this sub-optimality. These findings suggest a key principle for ensemble design: the predictive complementarity of the models is a more critical factor than their individual performance or the complexity of the aggregation method-a finding reinforced by our benchmark showing that even advanced strategies like calibrated voting and stacking failed to outperform the pruned ensemble. Our work thus positions the methodological pairing of ablation study and correlation analysis as an essential and pragmatic approach to optimize the performance of ensembles in computer vision.References
Padol, P. B. and Yadav, A. A. (2016). SVM classifier based grape leaf disease detection. Conference on Advances in Signal Processing (CASP), Pune, India, 2016, pp. 175-179.
https://doi.org/10.1109/CASP.2016.7746160
Vaishnnave, M. P., Devi, K. S., Srinivasan, P. and Jothi, G. A. P. (2019). Detection and Classification of Groundnut Leaf Diseases using KNN classifier, IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-5.
https://doi.org/10.1109/ICSCAN.2019.8878733
Mohanty, S. P., Hughes, D. P. and Salathe, M. (2016). Using deep learning for image-based plant disease detection. Front. Plant Sci. https://doi.org/10.3389/fpls.2016.01419
Too, E. C., Yujian, L., Njuki, S., and Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, pp. 272–279.
https://doi.org/10.1016/j.compag.2018.03.032
Hasan, R.I.; Yusuf, S.M.; Alzubaidi, L. (2020). Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion. Plants, 9, 1302. https://doi.org/10.3390/plants9101302.
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., Gechev, T., Hussain, T., Ali, F. (2023). An advanced deep learning models-based plant disease detection. A review of recent research. Front. Plant Sci., 14, 1158933.
https://doi.org/10.3389/fpls.2023.1158933
Akbar, M., Ullah, M., Shah, B., Khan, R.U., Hussain, T., Ali, F., Alenezi, F., Syed, I., Kwak, K.S. (2022). An effective deep learning approach for the classification of Bacteriosis in peach leave. Front. Plant Sci., 13, 1064854. https://doi.org/10.3389/fpls.2022.1064854
Zhao, Y., Sun, C., Xu, X., and Chen, J. (2022). RIC-net: A plant disease classification model based on the fusion of inception and residual structure and embedded attention mechanism, Computers and Electronics in Agriculture 193: 106644. https://doi.org/10.1016/j.compag.2021.106644
Chandra, M., Redkar, S., Roy, S., Patil, P. (2020). Classification of Various Plant Diseases Using Deep Siamese Network.
https://www.researchgate.net/publication/341322315
Iftikhar, M., Kandhro, I. A., Kausar, N., Kehar A., Uddin, M. and Dandoush A. (2024). Plant disease management: A fine-tuned enhanced CNN approach with mobile app integration for early detection and classification, Artif. Intell. Rev., vol. 57, no. 7, pp. 1-29.
https://doi.org/10.1007/s10462-024-10809-z
Ganaie, M., Hu, M., Malik, A., Tanveer, M., and Suganthan, P. (2022). Ensemble deep learning: A review. Eng. Appl. Artif. Intelligence, 115, 105151.
https://doi.org/10.1016/j.engappai.2022.105151
Li, H., Jin, Y., Zhong, J., and Zhao, R. (2021). A fruit tree disease diagnosis model based on stacking ensemble learning. Complexity, 2021, 6868592.
https://doi.org/10.1155/2021/6868592
Mathew, A., Antony, A., Mahadeshwar, Y., Khan, T., and Kulkarni, A. (2022). Plant disease detection using GLCM feature extractor and voting classification approach. Mat. Today, Proc. 58, Part 1, pp.407–415. https://doi.org/10.1016/j.matpr.2022.02.350
Palanisamy, S., and Sanjana, N. (2023). Corn leaf disease detection using genetic algorithm and weighted voting, Proceedings of the 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India. (ICAECA). pp. 1-6,
https://doi.org/10.1109/ICAECA56562.2023.10200196
Chaudhary, A., Thakur, R., Kolhe, S., and Kamal, R. (2020). A particle swarm optimization-based ensemble for vegetable crop disease recognition. Comput. Electron. Agricult., 178, 105747.
https://doi.org/10.1016/j.compag.2020.105747
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249.
https://doi.org/10.1002/widm.1249.
Giacinto, G., and Roli, F. (2001). Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognition, 34(9), pp.1879-1881. https://doi.org/10.1016/S0031-3203(00)00150-3
Tsoumakas, G., Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM), 3(3), pp. 1-13. https://doi.org/10.4018/jdwm.2007070101
Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A. (2004). Ensemble selection from libraries of models. Proceedings of the 21st International Conference on Machine Learning (ICML '04).
https://doi.org/10.1145/1015330.1015432
Astani, M., Hasheminejad, M. and Vaghefi, M. (2022). A diverse ensemble classifier for tomato disease recognition, Comput. Electron. Agricult., vol. 198, Art. no. 107054. https://doi.org/10.1016/j.compag.2022.107054
Shafik, W., Tufail, A. De Silva Liyanage, C. and R. Apong, A. A. H. M. (2024). Using transfer learning-based plant disease classification and detection for sustainable agriculture, BMC Plant Biol., vol. 24, no. 1, p. 136.
https://doi.org/10.1186/s12870-024-04825-y.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations (ICLR).
https://doi.org/10.48550/arXiv.2010.11929
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV48922.2021.00986
Liu, Z., Mao, H., Wu, C. Y., Feichtenhofer, C., Darrell, T., Xie, S. (2022). A ConvNet for the 2020s. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
https://doi.org/10.48550/arXiv.2201.03545
Chen, J., Zhang, D. Zeb, A. and Nanehkaran, Y. A. (2021). Identification of Rice plant diseases using lightweight attention networks, Expert Syst. Appl., vol. 169, Art. no. 114514. https://doi.org/10.1016/j.eswa.2020.114514
Ouamane, A., Chouchane, A., Himeur, Y., Miniaoui, S., Atalla, S., Mansoor, W. (2025). Optimized Vision Transformers for Superior Plant Disease Detection, IEEE Access, vol. 13, pp. 48552-48570.
https://doi.org/10.1109/ACCESS.2025.3547416
Singh Thakur, P., Khanna, P., Sheorey, T., and Ojha, A. (2022). Explainable vision transformer enabled convolutional neural network for plant disease identification: Plantxvit, arXiv:2207.07919. https://doi.org/10.48550/arXiv.2207.07919.
Tabbakh, A. and Barpanda, S. S. (2023). A deep features extraction model based on the transfer learning model and vision transformer ‘TLMViT’ for plant, IEEE Access, vol. 11, pp. 45377-45392.
https://doi.org/10.1109/ACCESS.2023.3273317
Singh, D., Padgett, E., & T. S. G., A. K. (2020). PlantDoc: A Dataset for Visual Plant Disease Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.48550/arXiv.1911.10317
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988.
https://doi.org/10.48550/arXiv.1708.02002
Kasneci, G. and Kasneci, E. (2024). Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers, arXiv preprint arXiv:2411.01645. https://doi.org/10.48550/arXiv.2411.01645
Grandini M., Bagli E., Visani G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.
https://doi.org/10.48550/arXiv.2008.05756
Ramaprasad, R., Raman, S. (2022). SEMFD-Net: A Stacked Ensemble for Multiple Foliar Disease Classification. Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD) pp. 241-245. https://doi.org/10.1145/3493700.3493719
Menon, V.; Ashwin, V.; Deepa, R.K. (2021). Plant disease detection using CNN and transfer learning. In Proceedings of the International Conference on Communication, Control and Information Sciences (ICCISc), Idukki, India, pp. 16–18; IEEE: Piscataway, NJ, USA, 2021; Volume 1, pp. 1–6.
https://doi.org/10.1109/ICCISc52257.2021.9484957
Puangsuwan T.; Surinta, O. (2021). Enhancement of plant leaf disease classification based on snapshot ensemble convolutional neural network. ICIC Exp Lett., 15(6), pp. 669–680.
https://doi.org/10.24507/icicel.15.06.669
Moupojou, E., Tagne, A., Retraint, F., Tadonkemwa, A., Dongmo, W., Tapamo, H., Nkenlifack, M. (2023). FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning. IEEE Access, 11, pp. 35398–35410.
https://doi.org/10.1109/ACCESS.2023.3263042
DOI:
https://doi.org/10.31449/inf.v50i8.10277Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.
All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.
Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.
