Kelp, as a kind of climate-friendly food, animal feed, and bioplastics material, has been gradually cultivated worldwide. In the process of kelp culture, clipping seedling operation is essential to guarantee the yield. Traditionally, the clipping seedling operation is conducted by hand with low production efficiency and high labor intensity. In this paper, a novel automatic clipping seedling machine is developed to improve efficiency. The machine consists of an automatic seedling clipping core device, an automatic seedling feeding device, an automatic seedling rope conveyor, and an automatic product storage device. The corresponding structure, mechanism, and control system are introduced and analyzed. The performances of the automatic machine are tested and compared to manual kelp seedling clipping. Compared to manual kelp seedling clipping, the automatic machine can improve efficiency by 35%. Additionally, the quality of kelp seedling clipping is investigated. The attaching force of the kelp seedling clipped by the automatic machine is 2.179 N (224.4 g $\times$ 9.8 N/Kg $\times$ ${10}^{-3}$). The rate of the clipped kelp seedlings falling off the rope is only 1.3%. These performances prove that the automatic machine Is feasible and can be used to improve clipping efficiency and ensure clipping quality at the same time.

Kelp, often referred to as a “sea vegetable”, holds substantial economic significance. Currently, the drying process for kelp in China primarily relies on outdoor sun-drying methods. Detecting kelp in the field presents challenges arising from issues such as overlapping and obstruction. To address these challenges, this study introduces a lightweight model, K-YOLOv5, specifically designed for the precise detection of sun-dried kelp. YOLOv5-n serves as the base model, with several enhancements implemented in this study: the addition of a detection head incorporating an upsampling layer and a convolution module to improve the recognition of small objects; the integration of an enhanced I-CBAM attention mechanism, focusing on key features to enhance the detection accuracy; the replacement of the CBS module in the neck network with GSConv to reduce the computational burden and accelerate the inference speed; and the optimization of the IoU algorithm to improve the identification of overlapping kelp. Utilizing drone-captured images of sun-dried kelp, a dataset comprising 2190 images is curated. Validation on this self-constructed dataset indicates that the improved K-YOLOv5 model significantly enhances the detection accuracy, achieving 88% precision and 78.4% recall. These values represent 6.8% and 8.6% improvements over the original model, respectively, meeting the requirements for the real-time recognition of sun-dried kelp.

China has assumed a leadership position in global kelp cultivation and export. However, the kelp harvesting process in China still relies primarily on manual labor, with only limited adoption of semi-mechanized devices. This dependence results in suboptimal efficiency and elevated labor intensity. The industry now faces an acute labor shortage driven by an aging workforce and rising labor costs, highlighting the urgent need for a fully mechanized harvesting solution. This paper comprehensively reviews current research on mechanized kelp harvesting devices for raft cultivation systems in China. It compares domestic and international practices in kelp seedling cultivation, cultivation models, and harvesting devices, with particular emphasis on the technical challenges hindering harvesting device development in China. Based on this analysis, we propose several recommendations, including the simultaneous advancement of cultivation model optimization and harvesting device innovation, the development of harvesting technologies aligned with specific downstream processing requirements, and the design of modular and multifunctional kelp harvesting vessels. Looking ahead, future research should prioritize integrating automation and intelligent systems, reflecting evolving trends in China’s marine aquaculture. Furthermore, to support China’s “dual carbon” goals, future harvesting systems should incorporate carbon-reduction features.

The fish pump is an important piece of power equipment for aquaculture, especially for deep-sea engineering vessels and cage culture. Fish pump research focuses on reducing fish body damage, improving survival rates, and increasing fish lifting efficiency. The research object in this paper is a new type of vacuum fish pump, with the aim of improving the hydraulic performance of the vacuum fish pump and reducing the damage to the fish body. The dependent variables include the dynamic change process of the flow state and flow field under diachronic conditions, the fluid simulation analysis of the vacuum pump body and the flow channel structure, the inlet flow rate of the fish pump, the negative pressure of the pipeline, and the impact force of the water flow on the inner wall of the tank. The independent variables include the operating conditions of the pump body and the fish pump. The Latin hypercube sampling method is used to extract 167 sets of calculation models for the independent variables, and multi-objective optimization is performed based on the NSGA-II algorithm for the hydrodynamic performance of the fish pump. On the basis of ensuring the fish body damage rate, the structural parameters of the vacuum fish pump with the optimal hydrodynamic performance under 167 sets of parameter values were obtained. The optimized parameters were then entered into the solver again, and the results showed that, in the optimal structural parameters under certain conditions, the direction of the incident water flow in the vacuum fish pump tank is close to the upper end of the tank body, which will reduce the speed of the fish-water mixed flow when entering the tank, thereby reducing the collision damage to the fish body. Currently, the water flow velocity at the water inlet is about 2.5 m/s, and the negative pressure value distribution gradient between the tank body and the water inlet pipeline is quite consistent, which can achieve good fish suction and fish lifting effects.

To address the challenges of labor-intensive and costly manual oyster spat insertion in longline-suspended farming, an automated oyster spat insertion device was designed based on negative pressure suction and bundling fixation technologies. Using this device as the experimental platform, a three-factor, three-level Box–Behnken experiment was conducted, with fixation mechanism inclination, negative pressure suction cup span, and horizontal distance between turnover and fixation mechanisms as the experimental factors. The performance of the device was evaluated using the effective fixation rate and damage rate as the experimental indicators. The quadratic polynomial regression models were established to assess the impact of these factors on operational performance, while the response surface method was employed to analyze the interaction effects between factors. Parameter optimization and experimental validation were also performed. The results indicate that the factors affecting the effective fixation rate, in order of significance, are as follows: fixation mechanism inclination, horizontal distance between turnover and fixation mechanisms, and negative pressure suction cup span. For the damage rate, the order of significance is as follows: fixation mechanism inclination > negative pressure suction cup span < horizontal distance between turnover and fixation mechanisms. The optimization results show that when the fixation mechanism inclination is set at 43°, the negative pressure suction cup span at 27 mm, and the horizontal distance between turnover and fixation mechanisms at 179 mm, the effective fixation rate reaches 92.08%, and the damage rate is 4.71%. The relative errors between the measured and model-predicted values are less than 5%, indicating that the regression models are reliable. This research provides valuable insights for advancing the mechanization of the oyster farming industry and replacing manual labor with mechanized equipment.