# Nano Aerial Vehicles for Tree Pollination
###### Abstract
Additionally, YOLO is known for its fast and efficient processing of object detection tasks, as it can detect objects in real time on a CPU or GPU. However, if the size of the dataset is relatively small, SVM or Long Short-Term Memory (LSTM) may be better suited for the task, as they tend to perform better than CNN or YOLO on smaller datasets. SVM and LSTM are less prone to overfitting on small datasets, whereas CNN and YOLO require larger datasets to learn complex features and avoid overfitting. Although the dataset of this work is small, the intention is to increase its size by adding different flower varieties and growing stages to the hundreds of thousands of images to increase the YOLO accuracy and drone swarm efficiency in the pollination work. YOLOv5, YOLOv7, and YOLOR share many standard features in their network structures. Here are the fundamental similarities of the network structures of these three models:  
* Utilisation of CNN architecture, which is well suited to image analysis tasks such as object detection;
* Division of the network into three parts. The backbone network extracts the features from the input image, the neck network connects the backbone to the head, and the head network generates the final object detections;
* Multi-scale processing, i.e., processing the input image at multiple scales, helps to improve performance by considering objects of different sizes;
* The head network generates the object detections by outputting the bounding boxes, class scores, and confidence scores for each object in the image;
* Upsampling layers in the head network increases the spatial resolution of the output, which helps to improve the accuracy of the object detections;
* Multi-task loss function that considers both the localisation and classification tasks involved in the object detections. Although they share some similarities, some key differences between these models set them apart. YOLOv5 has a hybrid architecture that combines the strengths of YOLO with those of feature pyramids. It includes several innovations, such as anchor-free detection and generalised anchor tuning, that help to improve its performance. However, YOLOv5 also has a more significant number of parameters. YOLOv7 is a lighter and faster version of YOLO that is designed to be more computationally efficient. This version has the smallest parameters and uses a more optimised architecture to reduce the required computation [9]. YOLOR uses a recurrent neural network (RNN) structure that allows the model to process multiple frames simultaneously and make predictions over time. The main difference between YOLOR and the other YOLO models is the use of residual connections, which address the problem of vanishing gradients and improve the overall performance [10]. In the literature, numerous works can be found that use YOLO models to detect objects in various [11; 12] scenarios. Currently, works are being developed that integrate YOLO neural networks into UAVs [13; 14]. This model has been applied to various problems in agriculture to address challenges such as crop weeds [15], yield prediction [16], and pest control [17], among others. In this work, DL is essential for detecting flowers. YOLO networks are widely used for object detection due to their fast and efficient performance. The choice of YOLOv5, YOLOv7, and YOLOR as the models for detecting flowers is likely due to their strengths and capabilities as object detection algorithms. This work aims to contribute to an open-source solution known as the Nano Aerial Bee (NAB) to enable further research and development on the use NAVs in an agricultural context. The NAB is a new open-source NAV with an autonomous flight used for tree pollination to assist bees.