Sugar Beet Seed Classification for Production Quality Improvement by Using YOLO and NVIDIA Artificial Intelligence Boards

Abstract All inputs are required for excellent and proper crop production, especially seed quality. In this way fewer disease and insect issues, increased seedling germination, uniform plant population and maturity, and better responsiveness to fertilizers and nutrients, leading to higher returns per unit area and profitability, and low labor costs could be possible. Because of this reason, NVIDIA Jetson Nano and TX2 artificial intelligence boards were used to test the efficiency of the YOLOv4 and YOLOv4-tiny models for sugar beet monogerm and multigerm seed classification for better production. YOLOv4-tiny outscored the other model based on FPS with 8.25–8.37 at NVIDIA Jetson Nano, 12.11–12.36 at NVIDIA TX2 artificial intelligence boards with accuracy 81–99% for monogerm seeds, and 89–99% for multigerm seeds at NVIDIA Jetson Nano, 88–99% for monogerm seeds, and 90–99% for multigerm at NVIDIA TX2 accuracy, respectively, implying that the YOLOv4 is more accurate but slow with based on FPS with 1.10–1.21 at NVIDIA Jetson Nano, 2.41–2.43 at NVIDIA TX2 artificial intelligence boards with 95–99% for monogerm seeds and 95–100% for multigerm seeds at NVIDIA Jetson Nano, 92–99% for monogerm seeds and 98–100% for multigerm seeds at NVIDIA TX2, respectively. As a result of the evaluations, NVIDIA Artificial Intelligence cards and YOLO deep learning model will be used effectively in classifying monogerm and multigerm sugar beet seeds, thus reducing seed loss with the help of NVIDIA Artificial Intelligence cards classification.