Deep learning and drone imagery for the automatic detection and quantification of common vole burrows in agricultural landscapes
[EN] Accurate detection of vole burrows is critical for monitoring rodent outbreaks and mitigating agricultural damage. This study compared deep learning (DL) and traditional supervised classification approaches -Random Forest (RF) and Support Vector Machines (SVM)- using high-resolution UAS imagery...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/167801 |
| Acceso en línea: | http://hdl.handle.net/10366/167801 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Common vole UAS imagery Burrows Object-oriented classifications Aprendizaje profundo Topillo común Imágenes aéreas no tripuladas Madrigueras Clasificaciones orientadas a objetos 1203.04 Inteligencia Artificial 5102.01 Agricultura 2506.16 Teledetección (Geología) 3308.08 Tecnología del Control de Roedores |
| Sumario: | [EN] Accurate detection of vole burrows is critical for monitoring rodent outbreaks and mitigating agricultural damage. This study compared deep learning (DL) and traditional supervised classification approaches -Random Forest (RF) and Support Vector Machines (SVM)- using high-resolution UAS imagery at two flight heights (15 m and 25 m). DL, implemented via convolutional neural networks, consistently outperformed classification methods, mainly due to a drastic reduction in misclassifications. The best DL configuration achieved a precision of 0.92, recall of 0.81, and an F1 score of 0.83, reflecting strong detection capability with limited false alarms. A sensitivity analysis further indicated that reducing the object-oriented classification to two classes (burrow vs. non-burrow) improved efficiency without major performance losses compared to a four-class scheme. Higher spatial resolution (15 m flights) enhanced detection across all methods, especially benefiting DL by improving recall without sacrificing precision. The findings highlight that DL is particularly suited for detecting relatively discrete objects like burrows, offering a reliable tool for early intervention in pest control strategies. In contrast, traditional classifications processed the imagery >95 % faster than the DL workflow but were hindered by excessive misclassifications that compromised overall accuracy. This work underscores the potential of UAS-based DL detection pipelines to improve pest monitoring in agricultural ecosystems, providing a scalable and accurate solution for early warning and management programs. Future research should focus on linking burrow density to vole population estimates to further assist in plague management. |
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