Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications

Abstract

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques.

Experimental Nonlinear and Incremental Control Stabilization of a Tail-Sitter UAV with Hardware-in-the-Loop Validation

“…Unmanned Aerial Vehicles (UAVs) have been increasing in popularity in the last decades, with broad scientific, industrial and military uses [1]. From a wide range of urban applications [2] to topographic mapping and surveillance operations [3], which are useful for agricultural purposes [4], for example, these aircraft are undeniably helpful and are becoming virtually indispensable in many fields. It is natural that a given application may influence the requirements for the UAV to be used.…”

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

“…Moreover, the dataset only classified forest and non-forest and did not accurately classify tree species, which is an area for future work. To achieve accurate classification of tree species, we will need to acquire more specific data and conduct more in-depth research on tree species classification [61]. Despite these limitations, our proposed data augmentation algorithm and high-resolution forest remote sensing image segmentation network have demonstrated promising results, and we believe that they will be useful in future remote sensing studies.…”

Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks

“…Table 1 presents an overview of the literature on tree segmentation published since 2010. For earlier papers, we refer readers to survey papers [2,9]. Our review grouped the papers into two domains: agriculture and digital forestry.…”

“…An important preliminary problem for successfully accomplishing these tasks is tree segmentation [2]. Depending on the final goal of the tasks, different problems must be formulated with respect to image acquisition and segmentation algorithms.…”