In the past decade, intelligent technologies have advanced rapidly, particularly due to improvements in automatic control, which have had a significant impact on forestry, as well as animal husbandry and the future of farm management. However, the degree of production and management mechanization in natural rubber plantations is low, especially since the methods of tapping still rely heavily on labor. The decrease of skilled rubber tappers and the increase in labor costs have led to the development of the mechanization of rubber tapping operations. The application of emerging intelligent agricultural technologies could provide an alternative in order to maximize the potential productivity of natural rubber. Based on this vision, we reviewed the literature on rubber tapping from the past decade for system implementation in rubber plantations. In this review, selected references on rubber tapping were categorized into several directions of research, including rubber tapping machines, the key technologies applied in tapping operations, and some related protective research, analyzing research works from 2010 to 2022 that focused on tapping methods. The review also discusses the application of intelligent agricultural technologies, such as the recognition of tapping trajectory and tapping path planning. A summary of challenges and future trends is also provided in this study. Based on the relevant research, the use of intelligent technologies in rubber tapping machines is still in its initial stage and has broad prospects. Through this study, we aim to provide a reference for researchers in the field of rubber tapping machines and thus to play a positive role in future rubber tapping.

The object detection algorithm is one of the core technologies of the intelligent rubber tapping robot, but most of the existing detection algorithms cannot effectively meet the tapping trajectory detection of natural rubber trees in the complex forest environment. This paper proposes a tapping trajectory detection method for natural rubber trees based on an improved YOLOv5 model to accomplish fast and accurate detection. Firstly, the coordinate attention (CA) mechanism is added to the Backbone network to embed the location information into the channel attention, which effectively improves the detection accuracy. Secondly, a module called convolution and GhostBottleneck (CGB) is designed, based on the Ghost module, to substitute the Cross Stage Partial Network (CSP) module in the Neck network, which ensures the detection accuracy while reducing model parameters. Finally, the EIoU loss function is introduced to enable a more accurate regression of the model. The experimental results show that the overall performance of the YOLOv5-CCE model outperforms the original YOLOv5 and other classical lightweight detection algorithms. Compared with the original YOLOv5 model, the YOLOv5-CCE model has a 2.1% improvement in mAP value, a 2.5% compression of model parameters, and a 7.0% reduction in the number of floating point operations (FLOPs). Therefore, the improved model can fully meet the requirements of real-time detection, providing a robust detection method for rubber tapping robots.

The ocean represents the cradle of life on Earth, making it essential to comprehend the complex interactions between marine animal behaviors and the physical microstructure of their environments in order to study their behavioral ecology. Due to the vastness of the ocean, traditional observational techniques are constrained by distance, which poses significant challenges for conducting extended and continuous research on marine animal behavior and ecology. To overcome these challenges, this paper introduces a behavior recording tag system incorporating temperature, pressure, and miniature inertial measurement unit (MIMU) sensors as data collection modules. These sensors are integrated with a main control module and a data storage module to gather and archive behavioral and environmental information. A combined positioning recovery method is proposed, developed, and validated to address the issue of retrieving the tag system post data collection. The behavior recording tag system’s performance was assessed through laboratory and pool tests. The findings show that the accuracy of temperature sensor is about 0.01 °C, the accuracy of pressure sensor is approximately 0.5% of full scale, the continuous data collection duration can extend to 3 days, and the recovery window time after surfacing exceeds 7 days, underscoring its viability as a marine animal behavior recorder.

To address the challenge in real-time communication mooring systems where clock synchronization and data transmission are difficult to achieve simultaneously over a single jacketed steel cable loop, this paper develops a dual-carrier inductively coupled communication (ICC) scheme based on the design concept of frequency division multiplexing (FDM). Two carrier signals with distinct frequencies and modulation schemes are loaded in parallel onto the same cable loop, using binary differential phase-shift keying (2DPSK) to carry minute-level data communication and binary phase-shift keying (2PSK) to carry microsecond-level clock synchronization broadcasts. To resolve inter-carrier crosstalk and channel attenuation, a single-stage multiple feedback (MFB) narrow bandpass filter and a three-stage analog front-end consisting of “passive high-impedance filtering, differential-to-single-ended conversion, and multistage active high-gain amplification” are designed in hardware, achieving efficient separation of hybrid signals and suppression of noise. In software, a combined demodulation architecture based on digital delay differential and zero-crossing decision, along with a digital Costas loop, is constructed to accomplish data demodulation and high-precision pulse-per-second (PPS) clock recovery. Laboratory and 300 m nearshore sea trial results demonstrate that the system achieves a packet reception ratio of over 89.60%, an error-free packet delivery ratio exceeding 83.24% with a bit error ratio below 0.22%, and maintains a clock synchronization error within 200 µs.