# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 2 State of the Art  
Numerous studies have been published that present various research on the use of vision systems for the classification and quality assessment of seeds. In the study [7], a simple method based on image processing and the use of an artificial neural network for rice grain classification was proposed. The classification is conducted by calculating the length of the major axis of the rice grain, the length of the minor axis, the area, the eccentricity, and the perimeter. Artificial neural networks were utilized to categorize rice grains into three levels with a classification accuracy of 98%. In the study [8], features such as area, perimeter, compactness, length, width, asymmetry, and the groove of the seed were utilized. The main machine learning methods used in this research are logistic regression, linear discriminant analysis (LDA), k-neighbors classifier, decision tree classifier, and support vector machine. The best results, achieving 95.8% accuracy, were obtained using LDA for the classification of various seeds. In the study [9], attention was focused on various visual attributes of rice seeds. By integrating these simple features with machine learning algorithms such as support vector machine, multi-layer perceptron, and Bayesian networks, rice grain identification was achieved. The classification accuracy ranged from 90.3% to 90.5%. Employing neural networks enhanced both accuracy and precision, with CNN emerging as the most effective neural network for seed classification. In the paper [10], research involving a CNN was presented. The dataset used for training the model consisted of images showing the top and bottom profiles of seeds. The CNN achieved a classification accuracy of 96.9%. In the article [11], a system is explored that uses a model to categorize 14 familiar seed types employing complex CNN techniques. By applying symmetry, uniformity is achieved in the resizing and labeling of images for extracting their characteristics. The model demonstrated a 99% accuracy level on the test set. In the study [12], image segmentation is achieved using the well-known deep learning technique, mask R-CNN, while classification is conducted using a new network referred to as the Soybean network (SNet). SNet, a highly compact model built on convolutional networks featuring mixed feature recalibration (MFR) modules, demonstrated an identification accuracy of 96.2% in tests. In the study [13], a method using a CNN as a universal feature extractor is presented. The extracted features were classified using various techniques, includingartificial neural networks, cubic and quadratic support vector machines, weighted k-nearest neighbors, boosted and bagged trees, and linear discriminant analysis. Models trained with CNN-extracted features demonstrated higher accuracy in classifying corn seed varieties compared to models based solely on simple features. The CNN-ANN classifier achieved the best results, with a classification accuracy of 98.1%, precision of 98.2%, recall of 98.1%, and an F1-score of 98.1%. The article [14] describes a computational methodology for quantitatively assessing soybean seed defects using a classification based on deep learning techniques. By developing a new, lightweight network for identifying defective soybean seeds, named SSDINet, it is possible to quickly and accurately distinguish between good and defective seeds. Research findings indicate that the SSDINet network achieved an accuracy of 98.6%. The article [15] presents an integration of hyperspectral imaging and deep learning for processing and classifying hyperspectral images (HSI) of rice seeds. The authors developed a method using a three-dimensional convolutional neural network (3D-CNN) to classify rice seed images exposed to high temperatures during the day and night. A deep neural network achieved an average accuracy of 94.8% and 91% across five different rice seed treatments with varying exposure durations to high temperatures.