eprintid: 28319 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/02/83/19 datestamp: 2026-04-29 19:28:12 lastmod: 2026-04-29 19:28:13 status_changed: 2026-04-29 19:28:12 type: article metadata_visibility: show creators_name: Abbas, Ahmed creators_name: Rehman, Saif Ur creators_name: Mahmood, Khalid creators_name: Gracia Villar, Santos creators_name: Dzul López, Luis Alonso creators_name: Smerat, Aseel creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: luis.dzul@uneatlantico.es creators_id: creators_id: title: A novel approach for disease and pests detection in potato production system based on deep learning ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Pests detection Disease detection Convolutional neural network Object detection Object classification deep learning 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. date: 2026-04 publication: Scientific Reports id_number: doi:10.1038/s41598-026-45575-1 refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-026-45575-1 access: open language: en citation: Article Subjects > Engineering 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 > Articles and Books Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production Open English 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. metadata Abbas, Ahmed; Rehman, Saif Ur; Mahmood, Khalid; Gracia Villar, Santos; Dzul López, Luis Alonso; Smerat, Aseel and Ashraf, Imran mail 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 document_url: http://repositorio.unib.org/id/eprint/28319/1/s41598-026-45575-1_reference.pdf