Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques

Nuhu Abdullahi; Umar Bello

doi:10.56201/ijcsmt.vol.11.no12.2025.pg220.231

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Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques

Nuhu Abdullahi, Umar Bello

Abstract

Plant diseases pose a significant threat to global food security, crop yield, and agricultural sustainability. Traditional disease diagnosis methods, relying on expert visual inspection and laboratory analyses, are often time-consuming, labor-intensive, and costly, limiting their scalability. Recent advances in artificial intelligence, particularly deep learning, offer automated and accurate solutions for plant disease detection. This review synthesizes current research on deep learning–based approaches, focusing on convolutional neural networks , hybrid CNN-transformer models, attention mechanisms, and generative data augmentation techniques. CNNs have demonstrated strong capabilities in extracting hierarchical features from leaf images, capturing complex patterns such as lesion shapes, color variations, and textures, thereby outperforming conventional machine learning and rule-based methods. Hybrid architectures and transformer-based models enhance feature extraction, particularly for subtle or visually similar disease symptoms. Data augmentation strategies, including generative adversarial networks , help address class imbalance and improve model generalization. Despite high reported accuracies, challenges remain, including limited dataset diversity, environmental variability, overfitting, lack of explainable AI, and computational constraints for mobile deployment. Future research should prioritize explainable models, multi-crop and multi-disease detection, lightweight architectures suitable for field applications, and integration with precision agriculture systems. Overall, deep learning–based plant disease detection presents a transformative approach for early diagnosis, timely intervention, and sustainable agricultural practices, offering the potential to reduce crop losses, enhance productivity, and contribute to global food security.

Keywords

Plant disease detectionConvolutional neural networksDeep learningPrecision agricultureVision transformers

References

Abade, A. S., Ferreira, P. A., & Vidal, F. d. B. (2020). Plant diseases recognition on images using convolutional neural networks: A systematic review. arXiv. https://arxiv.org/abs/2009.04365 Alzahrani, M. S., & Alsaade, F. W. (2023). Transform and deep learning algorithms for the early detection and recognition of tomato leaf disease. Agronomy, 13(5), 1184. Argüeso, D., Picon, A., Irusta, U., Medela, A., San Emeterio, M. G., Bereciartua, A., & Alvarez Gila, A. (2020). Few-shot learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture, 175, 105542. https://doi.org/10.1016/j.compag.2020.105542 Baniamerian, M., & Masoum, A. (2021). Review of artificial intelligence in plant disease detection. IEEE International Conference on Artificial Intelligence and Machine Vision (AIMV 2021). Chowdhury, M. J. U., Mou, Z. I., Afrin, R., & Kibria, S. (2025). Plant leaf disease detection and classification using deep learning: A review and a proposed system on Bangladesh’s perspective. arXiv. https://arxiv.org/abs/2501.03305 Elemmi, M. C., Hemanth Kumar, E. K., Raghu Nandan, R., Gaddi, A. V., & Pujar, S. (2025). Plant leaf disease detection using deep learning. Journal of Image Processing and Artificial Intelligence. [Publisher/DOI not specified]. Guerrero Ibañez, A., & Reyes Muñoz, A. (2024). Monitoring tomato leaf disease through convolutional neural networks. [Journal/Publisher not specified]. https://doi.org/10.3390/xxxxxx Ishak Pacal, I., Kunduracioglu, I., Alma, M. H., Deveci, M., Kadry, S., Nedoma, J., Slany, V., & Martinek, R. (2024). A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review, 57(304). https://doi.org/10.1007/s10462-024-10944-7 Kanakala, S., & Ningappa, S. (2025). Detection and classification of diseases in multi-crop leaves using LSTM and CNN models. arXiv. https://arxiv.org/abs/2505.00741 Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network applied to plant leaf disease classification. Agriculture, 11(8), 707. https://doi.org/10.3390/agriculture11080707 Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419 Prathibha Priyadarshini, G., & Zahoor Ul Huq, S. (2025). A systematic literature review on CNN-based deep learning models for plant disease detection and classification to enhance agricultural productivity. Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024). https://doi.org/10.2991/978-94-6463-718-2_100 Priyadarshini, G. P., & Zahoor Ul Huq, S. (2025). A systematic literature review on CNN-based deep learning models for plant disease detection and classification to enhance agricultural productivity. Advances in Computer Science Research, 120, 1207–1218. https://doi.org/10.2991/978-94-6463-718-2_100 Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468 Srikanth Veldandi, N., Nandini, & Reddy, K. M. (2024). Identification of plant leaf disease using CNN and image processing. Journal of Image Processing and Intelligent Remote Sensing, 4(4), 1–10. https://doi.org/10.55529/jipirs.44.1.10 Veldandi, S., Nandini, & Reddy, K. M. (2024). Identification of plant leaf disease using CNN and image processing. Journal of Image Processing and Intelligent Remote Sensing, 4(4), 1–10. https://doi.org/10.55529/jipirs.44.1.10

Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques

Abstract

Keywords

References

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