# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 1 Introduction  
The global demand for food continues to rise, while the need for environmental protection becomes increasingly important [1]. One of the most significant applications of modern technology is the use of computer vision for seed sorting [2]. This method allows for precise identification of seeds, sorting them by size, shape, or color. Each of these criteria directly impacts the health and viability of seeds [3, 4]. Seed size is often an indicator of maturity and germination potential [5]. Larger seeds typically contain more nutrients, promoting better development of young plants. By using computer vision, larger, more valuable seeds can be accurately separated from smaller ones, increasing the likelihood of uniform and healthy germination. Seeds with regular shapes and smooth surfaces are less prone to mechanical damage and diseases. Irregular shapes may indicate genetic defects or developmental issues, which can negatively affect plant growth [5]. Computer vision allows for the rapid and efficient elimination of such seeds from the sowing process. Seed color is often related to their health status. Color changes can signal the presence of diseases, infections, or seed aging. Sorting seeds by color enables the removal of those with potentially reduced viability, which is crucial for ensuring the good condition of crops. Proper seed sorting allows for the rapid and precise preparation of seeds for planting, directly resulting in better yields. Automating the seed sorting process significantly reduces the need for intensive manual labor, which is crucial in times when there is a shortage of workers willing to work in agriculture. Another advantage of this approach is the reduction of waste. Since machines can accurately select the best seeds, fewer seeds are wasted. This helps conserve resources and aligns with the principles of sustainable development. Inthe case of certain seeds, such as coffee beans, size and shape directly affect the nutritional quality and flavor profile of the final product. Larger coffee beans are often considered higher quality because they typically contain more oils and aromatic compounds, which are crucial for the intensity and depth of flavor [6].  
Seed selection utilizes several primary sorting methods, which vary depending on the specific requirements. The first and most traditional method is manual selection, where workers manually evaluate the seeds, analyzing their size, shape, color, and overall quality. Although time-consuming, this process allows for very precise detection of even minor defects. Another technique involves mechanical sorting mechanisms, such as vibrating screens, rotary drums, or conveyor belts. These devices are used to quickly and efficiently separate seeds by size and weight, but they may not be as precise in detecting subtler damages. A modern method is optical sorting, which uses advanced cameras and optical sensors to accurately analyze each seed [5]. This technology can detect even the smallest irregularities and defects, such as improper color, surface damage, or irregular shape. Technological advancements have also enabled the use of robots equipped with computer vision systems, capable of processing large quantities of seeds in a short time while maintaining high sorting precision. These advanced systems are increasingly used due to their efficiency, speed, and ability to significantly reduce human error.  
The application of advanced methods, including computer vision, is critical in seed-related industries as it enhances product uniformity and quality, ensures better utilization of raw materials, and minimizes waste. As a result, these processes become more automated, efficient, and economical, which is essential for meeting contemporary market and environmental demands.