In recent years, the investigation and exploitation of hydrothermal region and polymetallic mineral areas has become a hot topic. The emergence of underwater vehicle platforms has made it possible for new chemical sensors to be applied in marine in-situ detection. Laser-induced breakdown spectroscopy (LIBS), with its advantages of rapid real-time analysis, sampling without pretreatment, simultaneous multi-element detection and stand-off detection, has great potential in marine applications. In this paper, a newly more compact and lighter underwater LIBS system based on the LIBSea system named LIBSea II was developed and tested both in the laboratory and sea trials. The system consists of a Nd:YAG single-pulse laser at 1064 nm, a fiber spectrometer, optical layout, a power supply module and an internal environment sensor. The system is encapsulated in a pressure vessel (Φ 190 mm × L 588 mm) with an optical window on the end cap. Experimental parameters of the system including laser energy and delay time were firstly optimized in the laboratory. Then, field test of the system in nearshore was performed with various samples, including pure metal and alloy samples as well as a manganese nodule sample from deep sea, to verify the detection performance of the LIBSea II system. In 2019, the system was deployed on a remotely operated vehicle (ROV) of Haima for deep sea trial, and atomic lines of K, Na, Ca and strong molecular bands of CaOH from a carbonate rock sample were obtained for the first time at depths of 1400 m. These results show that the LIBSea II system has great potential to be used in deep-sea geological exploration.

In recent years, cabled ocean observation technology has been increasingly used for deep sea in situ research. As sophisticated sensor or measurement system starts to be applied on a remotely operated vehicle (ROV), it presents the requirement to maintain a stable condition of measurement system cabin. In this paper, we introduce one kind of ROV-based Raman spectroscopy measurement system (DOCARS) and discuss the development characteristics of its cabin condition during profile measurement process. An available and straightforward modeling methodology is proposed to realize predictive control for this trend. This methodology is based on the Autoregressive Exogenous (ARX) model and is optimized through a series of sea-going test data. The fitting result demonstrates that during profile measurement processes this model can availably predict the development trends of DORCAS’s cabin condition during the profile measurement process.

As a powerful in situ detection technique, Raman spectroscopy is becoming a popular underwater investigation method, especially in deep-sea research. In this paper, an easy-to-operate underwater Raman system with a compact design and competitive sensitivity is introduced. All the components, including the optical module and the electronic module, were packaged in an L362 × Φ172 mm titanium capsule with a weight of 20 kg in the air (about 12 kg in water). By optimising the laser coupling mode and focusing lens parameters, a competitive sensitivity was achieved with the detection limit of SO₄²⁻ being 0.7 mmol/L. The first sea trial was carried out with the aid of a 3000 m grade remotely operated vehicle (ROV) “FCV3000” in October 2018. Over 20,000 spectra were captured from the targets interested, including methane hydrate, clamshell in the area of cold seep, and bacterial mats around a hydrothermal vent, with a maximum depth of 1038 m. A Raman peak at 2592 cm⁻¹ was found in the methane hydrate spectra, which revealed the presence of hydrogen sulfide in the seeping gas. In addition, we also found sulfur in the bacterial mats, confirming the involvement of micro-organisms in the sulfur cycle in the hydrothermal field. It is expected that the system can be developed as a universal deep-sea survey and detection equipment in the near future.

In the scallop shucking industry, labor-intensive manual operations and inconsistent product quality remain persistent challenges. To address these issues, this study develops a scallop shucking system based on visual feedback temperature control. To support this system, systematic experiments are conducted to determine the baseline shucking method and its initial parameters. On this basis, the scallop doneness detection model SDD-RT-DETR, serving as the system’s core, integrates an enhanced backbone centered on the self-developed module HierarchicalRepBlock, a frequency-domain self-attention module AIFI-EDFFN, a neck featuring the self-developed EfficientBalanceFusion module as the feature fusion unit and the Converse2DC3 module as the feature extraction unit, and the Wise-DIoU loss function. A scallop doneness image dataset specifically constructed for this task was used to train and validate this model. Experimental results demonstrate that the model achieves 95.5% accuracy, 93.6% recall, and 96.1% mAP50, improving by 4.7%, 3.2%, and 4.2%, respectively, over the baseline model RT-DETR. Additionally, the model’s computational cost was reduced by 18.9%, and its parameters were reduced by 14.1% compared to the baseline model. This model provides real-time, accurate doneness assessment, thereby filling a gap in computer vision for scallop doneness detection. Furthermore, this study integrated this model into a water bath scallop shucking system with feedback temperature control, ensuring that the doneness of scallops after shucking is maintained within an optimal range. Batch tests show that this system achieves a 96.6% shucking rate and an 88.5% properly cooked rate, outperforming the conventional method. This improves the quality of automatically shucked scallops while offering a practical solution for the intelligent upgrading of the seafood processing industry.