# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 5 Methods  
In recent years, the development of deep neural networks (DNNs), particularly convolutional neural networks (CNNs), has led to significant advancements in the fields of image processing and computer vision. Due to their ability to detect features at various levels of abstraction, CNNs have become foundational in solving complex problems such as image recognition, object detection, and semantic segmentation. Image recognition, one of the primary applications of CNNs, involves classifying entire images into specific categories. CNNs automatically detect and learn important features from images during the training process, eliminating the need for manual feature design, which was necessary in traditional computer vision methods. Object detection extends the capabilities of image recognition by enabling the identification and localization of multiple objects within a single image. The YOLO algorithm has been a breakthrough in this field, offering a fast and accurate real-time object detection solution [23]. Unlike traditional approaches that first generate region proposals and then classify them, YOLO treats object detection as a single regression problem, predicting the classes and locations of objects directly from the entire image in one pass through the network. Semantic segmentation, another application of CNNs, involves assigning a class label to each pixel in an image, allowing for precise separation and understanding of different elements within a scene. Similar to object detection, CNNs can learn complex feature representations from images, enabling accurate pixel-level class label predictions. The YOLO architecture is based on an innovative \"one-shot\" approach to object detection and segmentation. It excels in simultaneously locating and classifying objects within an image, making it efficient for real-time applications. The model effectively balances high performance with good accuracy in image segmentation. Its single-stage approach allows for rapid localization and classification of objects in an image, making it widely used in applications requiring quick real-time analysis. Although there might be slightly lower precision in detecting smaller details, its ability to recognize multiple objects simultaneously makes it an attractive solution for various scenarios, such as urban monitoring systems and autonomous vehicle technologies. Due to these attributes, YOLO is a powerful tool, combining efficiency with satisfactory accuracy in practical applications. YOLO is an advanced object detection model comprised of several key components. The first is the powerful base model, CSPDarknet53, which serves as the backbone network responsible for feature extraction from images [24]. The model then uses a PANet-based Neck, which effectively combines information from different levels of abstraction [24]. The main part of the model (head) is based on spatial pyramid pooling (SPP) [25] and feature pyramid network (FPN) [26], allowing for context consideration at various scales. YOLOv8 is designed for multi-object detection, achieved through a prediction layer that generates bounding boxes and the predicted probabilities of class membership. The model outputs a tensor containing information about object locations and their classifications. For activation and normalization functions, YOLOv8 employs leaky ReLU and advanced normalization layers [27]. The model training process is based on optimizing a loss function, such as mean squared error (MSE). The entire model architecture has been optimized to balance detection precision and performance, making it highly effective for real-time object detection [27]. The model comes in various versions designated as YOLOv8_n, where \"n\" refers to the number of layers in the model. For instance, YOLOv8_s has fewer layers than YOLOv8_m, and YOLOv8_l has more layers than YOLOv8_m. Each of these versions differs in model size, which affects the number of parameters and network complexity.