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ObjectiveThe pneumatic rapeseed seeder easily inhales the dust generated during live broadcast operations into the seeding pipe, and then releases it along with the rapeseed. Consequently, when monitoring the rapeseed flow, dust interference can affect the flow, detection sensitivity and accuracy. This research aims to develop a dust removal rapeseed seeding rate monitoring system suitable for air-assisted pneumatic rapeseed seeders to improve the transparency and intelligence of the sowing process.MethodsThe monitoring system comprised a dust removal rapeseed seed flow detection device and a sowing monitoring terminal, which could be adjusted for different seeders widths by altering the number of detection devices. The rapeseed stream sensing structure operated on the principle of photoelectric induction. A delicate light layer was generated using an LED light source and a narrow slit structure. The convex lens condenses the light and directed it onto the sensing area of the silicon photovoltaic cell. When the rapeseed seeds pass through the sensing light layer, the silicon photovoltaic cell produced a voltage change signal. The signal converts it into a pulse signal that can be recognized by the microcontroller. A dust removal mechanism was designed by analyzing dust sources in the seeding system during normal field operation of the air-assisted rapeseed seeding machine and understanding the impact mechanism of the dust detection device on the accuracy of rapeseed flow monitoring. This mechanism employed a transparent plate to protect the photoelectric induction device in a relatively enclosed space and used a stepper motor screw mechanism to generate friction between the transparent plate and the dust removal cloth for effective dust removal. The appropriate size of the dust shield was determined by comparing its movement stroke with other structural dimensions of the detection device. The relationship between the silicon photocells voltage and detection accuracy was established through experiments at seeding frequencies of 10‒40 Hz. To ensure that the real-time detection accuracy was not less than 90%, the dust removal control threshold was set at 82% of the initial voltage value. In order to prevent congestion and data loss during data transmission and improve the scalability and compatibility of the monitoring system, data transmission between the detection device and the monitoring terminal was implemented based on the CAN2.0A communication protocol. The structural framework and monitoring terminal functions of the rapeseed sowing monitoring system were outlined. Software functions of the detection device were designed to meet the dust removal, communication, and rapeseed flow detection needs. The program execution process of the detection device was explained. In order to provide data support for the dust flow rate that should be controlled at various seeding frequencies during the bench test, experiments were conducted in the field to obtain theoretical data.Results and DiscussionsThe comparison bench test of the detection device indicates that with the average seeding frequency ranging from 12.4 to 36.3 Hz and the average dust flow rate ranging from 252 to 386 mg/s, the detection accuracy after two dust removal cycles without a dustproof and dust removal detection device was not higher than 80.2%. The dust detection device with dust removal got an accuracy rate of not less than 90.2%, and the average detection accuracy rate within a single dust removal cycle was not less than 93.6%. The seeding amount monitoring bench test showed that when the average seeding frequency was no higher than 37.6 Hz, the seeding rate monitoring accuracy was not less than 92.2%. Furthermore, the field sowing experiment results demonstrated that at a normal operating speed (2.8‒4.6 km/h) of the rapeseed direct seeder, with a field sowing frequency of 14.8‒31.1 Hz, the accuracy of sowing quantity monitoring was not less than 93.1%.ConclusionsThe rapeseed sowing quality monitoring system provides effective support for precise detection even when operating in dusty conditions with the pneumatic rapeseed direct seeder. In the future, by integrating positioning data, sowing information, and fertilization monitoring data through CAN bus technology, a comprehensive field sowing and fertilization status map can be created to further enhance the system's capabilities.

[Objective]Unmanned seeding of rapeseed is an important link to construct unmanned rapeseed farm. Aiming at solving the problems of cumbersome manual collection of small and medium-sized field boundary information in the south, the low efficiency of turnaround operation of autonomous tractor, and leaving a large leakage area at the turnaround point, this study proposes to build an unmanned rapeseed seeding operation system based on cloud-terminal high-precision maps, and to improve the efficiency of the turnaround operation and the coverage of the operation.[Methods]The system was mainly divided into two parts: the unmanned seeding control cloud platform for oilseed rape is mainly composed of a path planning module, an operation monitoring module and a real-time control module; the navigation and control platform for rapeseed live broadcasting units is mainly composed of a Case TM1404 tractor, an intelligent seeding and fertilizing machine, an angle sensor, a high-precision Beidou positioning system, an electric steering wheel, a navigation control terminal and an on-board controller terminal. The process of constructing the high-precision map was as follows: determining the operating field, laying the ground control points; collecting the positional data of the ground control points and the orthophoto data from the unmanned aerial vehicle (UAV); processing the image data and constructing the complete map; slicing the map, correcting the deviation and transmitting it to the webpage. The field boundary information was obtained through the high-precision map. The equal spacing reduction algorithm and scanning line filling algorithm was adopted, and the spiral seeding operation path outside the shuttle row was automatically generated. According to the tractor geometry and kinematics model and the size of the distance between the tractor position and the field boundary, the specific parameters of the one-back and two-cut turning model were calculated, and based on the agronomic requirements of rapeseed sowing operation, the one-back-two-cut turn operation control strategy was designed to realize the rapeseed direct seeding unit's sowing operation for the omitted operation area of the field edges and corners. The test included map accuracy test, operation area simulation test and unmanned seeding operation field test. For the map accuracy test, the test field at the edge of Lake Yezhi of Huazhong Agricultural Universit was selected as the test site, where high-precision maps were constructed, and the image and position (POS) data collected by the UAV were processed, synthesized, and sliced, and then corrected for leveling according to the actual coordinates of the correction point and the coordinates of the image. Three rectangular fields of different sizes were selected for the operation area simulation test to compare the operation area and coverage rate of the three operation modes: set row, shuttle row, and shuttle row outer spiral. The Case TM1404 tractor equipped with an intelligent seeding and fertilizer application integrated machine was used as the test platform for the unmanned seeding operation test, and data such as tracking error and operation speed were recorded in real time by software algorithms. The data such as tracking error and operation speed were recorded in real-time. After the flowering of rapeseed, a series of color images of the operation fields were obtained by aerial photography using a drone during the flowering period of rapeseed, and the color images of the operation fields were spliced together, and then the seedling and non-seedling areas were mapped using map surveying and mapping software.[Results and Discussions]The results of the map accuracy test showed that the maximum error of the high-precision map ground verification point was 3.23 cm, and the results of the operation area simulation test showed that the full-coverage path of the helix outside the shuttle row reduced the leakage rate by 18.58%-26.01% compared with that of the shuttle row and the set of row path. The results of unmanned seeding operation field test showed that the average speed of unmanned seeding operation was 1.46 m/s, the maximum lateral deviation was 7.94 cm, and the maximum average absolute deviation was 1.85 cm. The test results in field showed that, the measured field area was 1 018.61 m2, and the total area of the non-growing oilseed rape area was 69.63 m2, with an operating area of 948.98 m2, and an operating coverage rate of 93.16%.[Conclusions]The effectiveness and feasibility of the constructed unmanned seeding operation system for rapeseed were demonstrated. This study can provide technical reference for unmanned seeding operation of rapeseed in small and medium-sized fields in the south. In the future, the unmanned seeding operation mode of rapeseed will be explored in irregular field conditions to further improve the applicability of the system.

Background: Type 2 diabetes mellitus (T2DM) significantly contributes to sepsis, with patients suffering from both conditions exhibiting greater severity and higher mortality rates compared to those with sepsis alone. Elderly individuals in the intensive care unit (ICU) are particularly prone to these comorbidities. A nomogram prediction model was developed to accurately assess prognosis and guide treatment for elderly patients with sepsis and T2DM. Methods: Data from 1489 patients with sepsis and T2DM in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed and categorized into 28-days survival (n = 1156) and 28-days death groups (n = 333). The dataset's clinical characteristics were employed to create a nomogram predicting 28-days mortality in elderly ICU patients with sepsis and T2DM. The least absolute shrinkage and selection operator (LASSO) regression identified candidate predictors, followed by a multivariate logistic regression analysis incorporating variables with p <.05 into the final model. A nomogram was then constructed using these significant risk predictors. The model's discriminatory power was evaluated through a receiver operating curve (ROC) and the area under the curve (AUC). Additionally, model performance was assessed using a calibration plot and the Hosmer-Lemeshow goodness-of-fit test (HL test), and clinical utility was examined via decision curve analysis (DCA). Results: Risk factors incorporated into the nomogram included age, ICU length of stay, mean blood pressure (MBP), metastatic solid tumor, Sequential Organ Failure Assessment (SOFA) score, Logistic Organ Dysfunction System (LODS) score, blood urea nitrogen (BUN), and vasopressor use. The predictive model demonstrated robust discrimination, with an AUC of 0.802 (95% CI 0.768–0.835) in the training dataset and 0.753 (95% CI 0.697–0.809) in the validation set. Calibration was confirmed with the HL test (p >.05), and DCA indicated clinical usefulness. Conclusion: This new nomogram serves as a practical tool for predicting 28-days mortality among elderly ICU patients with sepsis and T2DM. Optimizing treatment strategies based on this model could enhance 28-days survival rates for these patients. [ABSTRACT FROM AUTHOR]