In consideration of offshore oil spill accidents, a mechanical method is a kind of widely used treatment methods to recover spilled oil at sea. It also has the advantages of low cost, convenient use, and environmental friendliness. In order to improve the recovery efficiency and oil content of liquid recovered, a novel mechanical spilled oil recovery philosophy coupling surface vortex and hydrocyclone separation was proposed, and a small-scale prototype was manufactured. Medium crude from Bohai oil field was applied as spilled oil to test the recovery property of the prototype skimmer. The experiment results show that the novel skimmer is able to recover spilled oil effectively on the sea surface and speed up the process of recovery. Pressure of overflow pipe is sensitive to pump frequency, flow rate of inlet, and split ratio. In addition, oil content at the overflow port is influenced by spilled oil amount on the surface and split ratio. Besides, linear relationship is found between the recovery efficiency and the split ratio. The experimental study can provide a technical reference for the treatment of a small amount of spilled oil on the water surface and also has great significance for the design of spilled oil recovery equipment.

Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations.

High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost–benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts/m² (100%), changed obviously (overall classification accuracies: 90.65–94.39% for RF and 89.78–93.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts/m²), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts/m² (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.

A non-touching intelligent control system of water intenerating equipment is designed. The combination of power electronic technology and microcontroller technology is realized. Moc3061 is used as photoelectric coupler and KS3-5 is used as TRIAC. The reliability of the whole system has been greatly improved by using the non-touching intelligent control technology. The principle, structure and software design of the system are introduced. Practical running results show that the system is not difficult to build up, featuring the low price, but the high performance and high reliability. It can be used as a universal non-touching control platform. So it has great spread value