# Nano Aerial Vehicles for Tree Pollination
###### Abstract
Nano Aerial Vehicle (NAV) pollination collects and distributes pollen from the site and is more advantageous than the alternatives. Currently, solutions based on robotics, automation, and the Internet of Things (IoT) are used to improve and optimise tasks in agriculture and forestry. Unmanned Aerial Vehicles (UAVs) are often applied to diverse agriculture activities. Several classifications are used based on size, weight, speed, and altitude, among others. The class selected classifies UAVs according to their weight and wingspan [4]. The chosen class must be able to balance the weight that can be placed onboard and the size of the propellers (which cause the movement of pollen). The NAV class is the most suitable for the intended pollination functions, with a weight of 3 to 50 g and a wingspan of 2.5 to 15 centimetres. However, UAVs are still underutilised in this context, although they can perform tasks more precisely than conventional UAVs. UAVs offer several advantages over traditional UAVs in agriculture, the main ones being:  
* NAVs are significantly smaller and lighter than traditional UAVs, reducing the risk of injury to people, animals, and property in the case of an accident or other malfunction. These characteristics make them more manoeuvrable and they can operate in tight spaces and near crops. * NAVs can fly closer to crops, which allows for more precise guidance and application such as fertiliser or pest control chemicals. * NAVs are typically quieter and cause fewer changes in their surroundings, which can be important in certain applications such as tree pollination. However, there are some crucial limitations of NAV systems, particularly when it comes to their use in agriculture and other real-world applications, the main constraints being:  
* NAVs have technological limitations such as limited flight time, limited payload capacity, and control difficulties;
* NAVs can be significantly affected by weather conditions such as wind, rain, and snow, making it difficult for them to fly and navigate. These limitations have caught the attention of researchers and engineers, who are actively working to address them through advances in technology and innovation. Recently, there have been some efforts to explore the potential use of NAVs as robotic bees [5; 6; 7]. The challenge remains open because of the restrictions on size and weight. An NAB should be able to detect and pollinate flowers, i.e., it should be equipped to pollinate the flowers. Pollination using a NAV can be a promising option because it increases pollination efficiency and, consequently, the yields of some crops if executed correctly. The efficiency and robustness of an NAB should be tested by considering pollination in different environments. Deep learning (DL) is a subfield of machine learning that has been increasingly applied in agriculture to address various challenges. YOLO (You Only Look Once) is a convolutional neural network (CNN)-based object detection algorithm. YOLO divides an input image into a grid and applies a CNN to each grid cell to predict the object bounding boxes and class probabilities. YOLO uses a single-pass approach to predict both the object class probabilities and bounding box coordinates, making it more efficient and accurate for object detection tasks compared to other models [8]. YOLO's ability to detect multiple objects in a single image may be an advantage over other models for highly complex data such as images with many objects or intricate patterns. YOLO uses a grid-based approach to detect objects within the image at different scales and locations. In contrast, Support Vector Machines (SVM)-based solutions and other convolutional neural network (CNN)-based approaches may be better suited for tasks that require more nuanced feature extraction and pattern recognition. It should be noted that SVM cannot extract/learn features of a dataset; therefore, the design of a feature extraction system requires more human effort.