Deep Learning Segmentation Models for UAV-Based Detection of Crop Damage in Rapeseed Using RGB Imagery

Abstract

The objective of this study was to evaluate the accuracy of detecting crop damage caused by wild boar in rapeseed fields using UAV (unmanned aerial vehicle)-derived RGB (red, green and blue) imagery and deep learning segmentation models. The experiments were conducted on rapeseed crops at full maturity shortly before harvest in central-western Poland in 2021. Four convolutional neural network architectures—U-Net (U-shaped network), U-Net++, DeepLabV3+ (deep learning + labelling), and PSPNet (Pyramid Scene Parsing Network)—were benchmarked using two input configurations: RGB imagery alone and RGB combined with the topographic position index (TPI) derived from a digital surface model (DSM). Model performance was assessed using overall accuracy, F1-score (harmonic mean of precision and recall), and Intersection over Union (IoU), with class-specific metrics reported to provide a realistic evaluation of damaged-area detection. For RGB-only data, overall accuracy ranged from 0.957 to 0.972, while damaged-class F1 and IoU reached 0.752 and 0.603, respectively, for the best-performing model (U-Net). When RGB data were supplemented with TPI, overall accuracy and damaged-class metrics changed only slightly, indicating limited benefit from the topographic feature under these field conditions. Non-damaged crop areas were consistently well-classified (F1 > 0.977, IoU > 0.955). These results confirm that UAV-based RGB imagery enables reliable late-season assessment of wildlife-induced crop damage, and that reporting class-specific metrics in spatially independent test sets is essential for realistic performance evaluation.

Plant phenotyping relevance

UAV画像と深層学習セグメンテーションを用いて、ナタネの野生動物被害という植物状態を推定し、複数モデルをベンチマーク・性能評価しているため、フェノタイピング手法が中心である。

Code and data availability

The paper's UAV RGB orthomosaic, DSM/TPI rasters, and manual damage annotations are paper-specific phenotyping assets, but the Data Availability Statement restricts them to on-request access due to privacy reasons. No public repository, dataset URL, or author code deposit is provided; the Google Colab link is a generic

No evidence-backed public reproduction asset is currently recorded.