The combination of machine learning and products obtained by Remote Piloted Aircraft Systems (RPAS) has stood out in the forest sector in the last decade due to the wide range of applications and contributions to the classification and modelling of spatial attributes. The literature presents an overview of these applications, their limitations, and the perspectives related to the theme. Many applications are promising and have different approaches in practices such as identifying rare or invasive trees species, quantifying biomass, early detection of diseases, monitoring deforestation and forest fires, among others. In this context of large data sets generated by RPAS surveys, observed significant influence of deep learning methods, mainly due to their ability to automatically extract spatial characteristics and design flexibility, as in the case of different Neural Networks (NN) architectures. Other algorithms such as Random Forest (RF) and Support Vector Machine (SVM) also occupy a prominent position in this context, having a proven ability to deal with complex problems. However, reduction of computational load, consolidation of analysis protocols, automated selection of predictors, use of proprietary software, and expansion of the research scope are some of the challenges that still need to be overcome to disseminate and improve the use of RPA data sets. Thus, computer vision through deep learning networks emerges as a promising approach in object-based image analysis, considering its ability to recover patterns, even when operating in less complex databases, such as RGB compositions.

Accurate and low-cost tree inventories in forest plantations are essential for an effective production management. Stimulated by recent advancements in Unmanned Aerial Vehicle (UAV) imagery coupled with artificial intelligence, and by the interest in developing models capable of supporting decision-making on silvicultural and forest management, this study aimed to evaluate the performance of different vegetation indices in detecting Eucaliptus saligna individuals by using an improved deep learning model. The tree-individual detection model was created using the YOLOv8n algorithm using UAV RGB images and vegetation indices (VI) generated from the multispectral sensor onboard the UAV. Nine VIs were selected for training (65%) and testing (35%) the models. The proposed framework demonstrated that the MPRI, PSRI, and NDVI indices achieved an F1 score of 0.98 and a precision of 0.97 for detecting E. saligna individual trees six months after planting. Our study demonstrates the robustness of the proposed framework and recommends the application of the MPRI index in individual tree detection due to its efficient performance, cost-effectiveness, and simplicity, as it only utilizes regions of the visible spectrum.

In recent years, plant counting using data collected by sensors embedded in remotely piloted aircraft systems (RPAS), combined with machine learning algorithms, has become popular in the agroforestry sector, especially in crop planning, crop production estimation, among other applications. This study aimed to perform a systematic review of the literature on plant counting in the agricultural and forestry sector that uses data from sensors embedded in RPAS. We sought to identify the principal bibliometric indicators of scientific production and, through content analysis, the main characteristics and trends of the studies. A total of 33 scientific articles obtained on the Scopus and Web of Science platforms were used. Then, a content qualitative analysis of each article was conducted to identify the main thematic categories: agricultural and forest species, platform and sensors, software, and algorithm. There was an increase in scientific publications as of 2017. The USA presented the higher number of researches performed, with eight publications. There was a significant presence of RGB (Red, Green and Blue) sensors followed by multispectral. The algorithms Convoluctional Neural Network (CNN), Structure from Motion (SfM), and K-means stood out for the recurrence of use, either singularly or associated. Studies with this purpose drive new research development, where this technology utilization is revealed as a potential instrument to understand the usage trends, subsidize and encourage the information acquisition, promoting improvements and progress for research in the agroforestry scope.