# Nano Aerial Vehicles for Tree Pollination
###### Abstract  
Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command. precision agriculture; pollination; nano aerial vehicles; deep learning 202312202312202312202312202312202312202312202023122022022022022022202220  
Insects constitute the majority of pollinating animals, with honey bees (_Apis mellifera_ L.) being the principal pollinators of several crop species [2]. However, this species' foraging behaviour is distinctive because bees are sensitive to several factors. This behaviour is closely related to the bee colony and the environment. Many factors can impact foraging activity and they are divided into two main groups: in-colony and out-colony factors. The first group (in-colony factors) refers to factors such as the queen's presence, the queen's case (virgin or mated), infection of foragers with diseases or parasites, and the genotype of the bee strains, among others. Regarding the out-colony factors, bees are sensitive to reward volume, environmental factors (temperature and humidity), and insecticides, among others. The presence of natural enemies (e.g., pollen beetles), predators (e.g., hornets), and other bee communities also influences bee behaviour [3]. Honey bees are sensitive to various factors so there is a need to complement natural pollination. The lack of pollination is currently addressed with alternatives such as artificial pollination and self-fertile plants. Artificial pollination involves a tractor with a tilter spraying pollen (often imported). However, the disadvantages of this technique are the significant waste of pollen and the difficulty of controlling imported pollen. The use of self-fertile plants drastically limits the supply, reducing the choice of varieties with a capacity for self-pollination.