Sunflower Seed Variety and Quantity Identification Using YOLOv8 and DeepSORT Algorithm

The field of agriculture has slowly been integrating the use of technology with their daily operations to its advantage. Sunflower seeds, which are a popular mass-produced agricultural product, also employ these emerging techniques. This research aims to introduce a computer vision-based system designed to automate the classification and counting of sunflower seeds by leveraging YOLOv8 and DeepSORT algorithms. The system aims to identify and quantify three distinct seed varieties: Giant, Dwarf F1, and Mammoth Grey seeds using a Raspberry Pi-powered hardware setup. This setup was also used to capture high-quality images of the sunflower seeds which made up the dataset used. The researchers will utilize, along with the Raspberry Pi. a USB webcam to capture the video feed of the sunflower seeds and an LCD monitor to display the seed count and seed variety. Upon running multiple trials and utilizing a confusion matrix to analyse the system accuracy, the system was able to achieve an overall accuracy of 91.11% for its classification and 97.56% for its counting accuracy.