Abstract
Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean [email protected] of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.
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Data availability
The data can be requested from the corresponding authors.
References
Afzaal, H. et al. Detection of a potato disease (early blight) using artificial intelligence. Remote Sens. 13(3), 1–17 (2021).
Astani, M., Hasheminejad, M. & Vaghefi, M. A diverse ensemble classifier for tomato disease recognition. Comput. Electron. Agric. 198(4), 107054 (2022).
Upadhyay, N. & Gupta, N. Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified resnext approach. Environ. Monit. Assess. 196(7), 610 (2024).
Upadhyay, N., Sharma, D.K., & Bhargava, A. 3sw-net: A feature fusion network for semantic weed detection in precision agriculture. Food Anal. Methods 1–17 (2025)
Upadhyay, N., & Bhargava, A. Artificial intelligence in agriculture: applications, approaches, and adversities across pre-harvesting, harvesting, and post-harvesting phases. Iran J. Comput. Sci. 1–24 (2025).
Chen, Z. et al. Plant disease recognition model based on improved yolov5. Agronomy 12(2), 365 (2022).
Upadhyay, N., & Gupta, N. Seglearner: A segmentation based approach for predicting disease severity in infected leaves. Multimedia Tools Appl. 1–24 (2025).
Coşkun, Ö.F., Aydın, A., Toprak, S., & Başak, H. Rootstock-mediated salinity resilience in cucumber (cucumis sativus l.): integrating physiological traits, genomic stability and machine learning. BMC Plant Biol. (2025).
Toprak, S. & Coşkun, Ö. F. Machine learning-based evaluation of nutrient distribution in grafted cucumber plants. PhytoTalks 2(3), 457–466 (2025).
Jiang, P., Ergu, D., Liu, F., Cai, Y. & Ma, B. A review of yolo algorithm developments. Proc. Comput. Sci. 199, 1066–1073 (2021).
Islam, M. N., Rahman, M. M., Islam, M. M. & Ullah, M. A. Performance evaluation of hybrid efficientnetv2b3 and vision transformer model for potato leaf disease detection. BMC Plant Biol. 25(1), 167 (2025).
Kumar, A. & Shukla, P. Deep cnn feature extraction and wrapper-based svm classification for potato disease recognition. Revue d’Intelligence Artificielle 38(2), 309–317 (2025).
Ghosh, R., & Sinha, S. Transfer learning-based potato leaf disease detection using densenet201. arXiv preprint (2024).
Ali, M., Khan, A., Shah, F. & Zhang, Z. A convolutional neural network-based framework for early and late blight detection in potato leaves. Artif. Intell. Rev. 56(4), 7823–7841 (2023).
Kumar, S., Yadav, & R., Sharma, V. Potatopestnet: A novel cnn-based deep learning model for potato pest classification. arXiv preprint (2023).
Iqbal, M. & Khan, N. Explainable ai with resnet for interpretable potato leaf disease detection. Front. Artif. Intell. 7, 1449329 (2024).
Abbas, A., Maqsood, U., Rehman, S., Mahmood, K. & AlSaedi, T. An artificial intelligence framework for disease detection in potato plants. Eng. Technol. Appl. Sci. Res. 14(1), 12628–12635 (2024).
Karakan, A. Potato beetle detection with real time and deep learning. Processes 12(9), 2038 (2024).
Zhu, H. et al. Potato disease detection and prevention using multimodal ai and large language model. Comput. Electron. Agric. 229, 109824 (2025).
Poyato, D.H., Rull, J.D., Montes, R., Hern ndez, I., Barrio, I., Echeverria, C.P., Tardaguila, J., Herrera, & F., Herrera Poyatos. Small_data deep learning methodology for in field disease detection. arXiv preprint (2024)
Sinamenye, J. H., Chatterjee, A. & Shrestha, R. Potato plant disease detection: leveraging hybrid deep learning models. BMC Plant Biol. 25(1), 647 (2025).
Nishankar, S. et al. Vit-rot: Vision transformer-based robust framework for tomato leaf disease recognition. AgriEngineering 7(6), 185 (2025).
Astuti, T., Umar, A.N., Wahyudi, R., & Rifai, Z. Enhancing potato leaf disease detection: Implementation of convolutional vision transformers with synthetic datasets from stable diffusion. JOIV: Int. J. Inf. Vis. 8(4), 2054–2065 (2024).
Zhang, H. et al. Vmamba for plant leaf disease identification: design and experiment. Front. Plant Sci. 16, 1515021 (2025).
Yuan, C. et al. Mamba-yolo-ml: A state-space model-based approach for mulberry leaf disease detection. Plants 14(13), 2084 (2025).
Ilsever, M. & Baz, I. Consistency regularization based semi-supervised plant disease recognition. Smart Agric. Technol. 9, 100613 (2024).
Mu, J., Feng, Q., Yang, J., Zhang, J. & Yang, S. Few-shot disease recognition algorithm based on supervised contrastive learning. Front. Plant Sci. 15, 1341831 (2024).
Faria, F.T.J., Moin, M.B., Alam, M.S., Wase, A.A., Sani, M.R., & Hasib, K.M. Potatogans: Utilizing generative adversarial networks, instance segmentation, and explainable ai for enhanced potato disease identification and classification. arXiv preprint arXiv:2405.07332 (2024).
Nagarathinam, T., Venkatesan, M.T., & Arulmozhi, K. A unified survey of supervised, unsupervised, and semi-supervised learning techniques for plant leaf disease detection.
Shattock, R. Compendium of potato diseases (WR Stevenson, Wiley Online Library, 2002).
Taliansky, M., Mayo, M. A. & Barker, H. Potato leafroll virus: a classic pathogen shows some new tricks. Mol. Plant Pathol. 4(2), 81–89 (2003).
Sarki, R. et al. Image preprocessing in classification and identification of diabetic eye diseases. Data Sci. Eng. 6(4), 455–471 (2021).
Sharma, P., Hans, P., & Gupta, S.C. Classification of plant leaf diseases using machine learning and image preprocessing techniques. In Proceedings of the Confluence 2020 – 10th International Conference on Cloud Computing, Data Science and Engineering, 20, 480–484 (2020).
Jeong, H.J., Park, K.S., & Ha, Y.G. Image preprocessing for efficient training of yolo deep learning networks. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 635–637 (2018).
Government of Pakistan: District-wise map of the Punjab, Pakistan. https://www.surveyofpakistan.gov.pk/SiteImage/Downloads/province_map_punjab.pdf.
Google Earth: University of Agriculture Faisalabad, Okara Campus, Pakistan. https://earth.google.com/web/search/university+of+agricultureokara+campus/@30.8510545,73.5330093,186.79655602a,713.32702128d,35y,0h,0t,0r/data=CpUBGmcSYQolMHgzOTE4MGJkMDBmMDNmZmZmOjB4MmRlNWEwYzJiZGZiYWIyNBkXnSy13tk-QCGw_QnTHGJSQComdW5pdmVyc2l0eSBvZiBhZ3JpY3VsdHVyZSBva2FyYSBjYW1wdXMYAiABIiYKJAkaeJEa7x82QBEXeJEa7x82wBn7cCN0T-NJQCE0DRqS3VRKwEICCAE6AwoBMEICCABKDQj___________8BEAA?authuser=0.
Google Earth: Koont Research Farm, PMAS-University of Arid Agriculture Rawalpindi, Pakistan. https://earth.google.com/web/search/Koont+Research+Farm,+PMAS+-+University+of+Arid+Agriculture+Rawalpindi,+Chakwal+Rd,+Mandra,+Pakistan/@33.1166308,73.0111446,527.00020069a,694.15106496d,35y,0h,0t,0r/data=CtQBGqUBEp4BCiUweDM4ZGZmN2Q4NzRjZjhkZDk6MHhlN2U5ZWJiNmFkZmQ2OTNhGSdHpujtjkBAIQgbQ6W2QFJAKmNLb29udCBSZXNlYXJjaCBGYXJtLCBQTUFTIC0gVW5pdmVyc2l0eSBvZiBBcmlkIEFncmljdWx0dXJlIFJhd2FscGluZGksIENoYWt3YWwgUmQsIE1hbmRyYSwgUGFraXN0YW4YAiABIiYKJAk9sqT5hdo-QBENFWZsN9k-QBlBg-OFe2JSQCEieDAgvmFSQEICCAE6AwoBMEICCABKDQj___________8BEAA?authuser=0.
Kaur, R. et al. Yolo-leafnet: a robust deep learning framework for multispecies plant disease detection with data augmentation. Sci. Rep. 15(1), 28513 (2025).
Qin, R., Wang, Y., Liu, X. & Yu, H. Advancing precision agriculture with deep learning enhanced sis-yolov8 for solanaceae crop monitoring. Front. Plant Sci. 15, 1485903 (2025).
Li, D., Ahmed, F., Wu, N. & Sethi, A. I. Yolo-jd: A deep learning network for jute diseases and pests detection from images. Plants 11(7), 937 (2022).
Mamun, A.A., Zhang, M., Ahmedt-Aristizabal, D., Hayder, Z., & Awrangjeb, M. Statespace-ssl: Linear-time self-supervised learning for plant disease detection. arXiv preprint arXiv:2512.09492 (2025).
Mamun, A.A., Zhang, M., Ahmedt-Aristizabal, D., Hayder, Z., & Awrangjeb, M. Psmamba: Progressive self-supervised vision mamba for plant disease recognition. arXiv preprint arXiv:2512.14309 (2025).
Abid, M.S.Z., Jahan, B., Al Mamun, A., Hossen, M.J., & Mazumder, S.H. Bangladeshi crops leaf disease detection using yolov8. Heliyon 10(18) (2024).
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This study was funded by the European University of Atlantic.
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AA idea, formal analysis, writing original draft. SUR data curation, idea, writing original draft. KM methodology, data curation, formal analysis. SGV funding acquisition, investigation, visualization. LADL software, visualization, investigation. AS project administration, software, methodology. IA supervision, validation, writing – review and editing. All authors read and approved the final manuscript.
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Abbas, A., Rehman, S.U., Mahmood, K. et al. A novel approach for disease and pests detection in potato production system based on deep learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-45575-1
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DOI: https://doi.org/10.1038/s41598-026-45575-1
Keywords
- Pests detection
- Disease detection
- Convolutional neural network
- Object detection
- Object classification
- deep learning
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