Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery

Abstract

Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated by the need to detect fallen trees around the lake, two well-established deep neural networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN) and Retina-Net were used for fallen tree detection. A total of 7,590 annotations of three classes on 256 x 256 image patches were used for this study. Experimental results show the relevance of deep learning in this context, with Retina-Net model achieving 38.9% precision and 57.9% recall.

Plant phenotyping relevance

UAV画像と深層学習によって個体レベルの倒木・劣化状態を検出する手法を中心に評価しており、植物状態の取得・推定方法が主要な貢献です。

Code and data availability

The paper describes a UAV dataset of fallen Acacia xanthophloea trees (9,056 patches, 7,590 annotations) and models trained with Detectron2, but provides no public deposit, availability statement, or authors' URL for the dataset, annotations, code, or trained models. The only URLs mentioned (labelImg, Detectron2) are,

No evidence-backed public reproduction asset is currently recorded.