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

A novel approach for disease and pests detection in potato production system based on deep learning
type: AcademicArticle
type: Article
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
is part of: Scientific Reports
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: A novel approach for disease and pests detection in potato production system based on deep learning (Texto)
subject: Engineering

A novel approach for disease and pests detection in potato production system based on deep learning (Texto)
type: Document
has file: s41598-026-45575-1_reference.pdf
has part: s41598-026-45575-1_reference.pdf

Scientific Reports
type: Collection
issn: 20452322

Santos Gracia Villar
type: Person
given name: Santos
family name: Gracia Villar

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Aseel Smerat
type: Person
given name: Aseel
family name: Smerat

Luis Alonso Dzul López
type: Person
given name: Luis Alonso
family name: Dzul López

Ahmed Abbas
type: Person
given name: Ahmed
family name: Abbas

Saif Ur Rehman
type: Person
given name: Saif Ur
family name: Rehman

Khalid Mahmood
type: Person
given name: Khalid
family name: Mahmood

HTML Summary of #28319 A novel approach for disease and pests detection in potato production system based on deep learning
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primary topic: A novel approach for disease and pests detection in potato production system based on deep learning

Engineering
type: Concept
preferred label: Ingeniería @es