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As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China.

This study proposes a positive pressure re-acceleration assisted seeding mechanism and analyzes the motion mechanism of corn seeds during the seeding process. By employing the CFD-EDEM gas-solid coupling simulation analysis method, the fluid characteristics, initial ejection velocity of seeds, seed dropping time difference, and sowing position difference in the seeding mechanism under different structural parameters of the air pressure valve body were investigated. The optimal structural parameters of the air pressure valve body were determined (nozzle gap c = 0.6 mm, throat constriction diameter d = 16 mm, and throat constriction length l = 44 mm). A multi-factor experimental method was used to explore the effects of airflow pressure, forward speed during sowing, and sowing distance on sowing performance, aiming to identify the optimal working parameters for the positive pressure re-acceleration seeding mechanism. High-speed camera technology was used to record and analyze the seed movement process. The results indicate that an increase in positive pressure within the seed guide tube shortens the sowing time of corn seeds, reduces the coefficient of variation of seed dropping time difference, and effectively improves the consistency of sowing distance. The optimal parameters are a forward speed of 8 km/h, sowing distance of 20 cm, airflow pressure of 10 kPa, with a sowing distance coefficient of variation of 7.56%, and a seed dropping time difference coefficient of variation of 5.35%. [ABSTRACT FROM AUTHOR]

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester. [ABSTRACT FROM AUTHOR]

Experimental and linear stability theory (LST) investigation of boundary layer transition on a flat plate was conducted with a flow of Mach number 5. The temperature distributions and second-mode disturbances on the flat plate surface at different unit Reynolds number (Reunit) values were captured by infrared thermography and PCB technology, respectively, which revealed the transition location of the flat-plate boundary layer. The PCB sensors successfully captured the second-mode disturbances within the boundary layer initially at a frequency of about 100 kHz, with a gradually expanding frequency range as the distance travelled downstream increased. The evolution characteristics of the second-mode instabilities were also investigated by LST and obtained for the second mode, ranging from 100 to 250 kHz. The amplitude amplification factor (N-factor) of the second-mode instabilities was calculated by the eN method. The N-factor of the transition location in the wind tunnel experiment predicted by LST is about 0.98 and 1.25 for Reunit = 6.38 × 106 and 8.20 × 106, respectively. [ABSTRACT FROM AUTHOR]

Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester's harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester's harvesting region. This study provides key information for adaptive header control of combine harvesters. [ABSTRACT FROM AUTHOR]

This article, titled "[Retracted] Calpastatin peptide attenuates early brain injury following experimental subarachnoid hemorrhage," was published in the journal Experimental & Therapeutic Medicine in 2020. However, it has since been retracted due to concerns raised by a reader. The reader pointed out that an image in the article was very similar to an image in another article published in a different journal. After discussing the issue with the authors, the editor decided to retract the paper. The authors accepted this decision, and the editor apologizes for any inconvenience caused. [Extracted from the article]