Deep transfer learning-based computer vision for real-time harvest period classification and impurity detection of <italic>Porphyra haitnensis</italic>.

Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify <italic>Porphyra haitnensis</italic> into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.Graphical abstract: Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify <italic>Porphyra haitnensis</italic> into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it. [ABSTRACT FROM AUTHOR]