A novel approach for disease and pests detection in potato production system based on deep learning

Abbas, Ahmed and Rehman, Saif Ur and Mahmood, Khalid and Gracia Villar, Santos and Dzul López, Luis Alonso and Smerat, Aseel and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2026) A novel approach for disease and pests detection in potato production system based on deep learning. Scientific Reports. ISSN 2045-2322

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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 mAP@0.5 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.

Item Type:	Article
Uncontrolled Keywords:	Pests detection Disease detection Convolutional neural network Object detection Object classification deep learning
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	13 Apr 2026 08:31
Last Modified:	13 Apr 2026 08:31
URI:	http://repositorio.funiber.org/id/eprint/28319

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