A Compound Sensor for Simultaneous Measurement of Packing Density and Moisture Content of Silage

Packing density and moisture content are important factors in investigating the ensiling quality. Low packing density is a major cause of loss of sugar content. The moisture content also plays a determinant role in biomass degradation. To comprehensively evaluate the ensiling quality, this study focused on developing a compound sensor. In it, moisture electrodes and strain gauges were embedded into an ASABE Standard small cone for the simultaneous measurements of the penetration resistance (PR) and moisture content (MC) of silage. In order to evaluate the performance of the designed sensor and the theoretical analysis being used, relevant calibration and validation tests were conducted. The determination coefficients are 0.996 and 0.992 for PR calibration and 0.934 for MC calibration. The validation indicated that this measurement technique could determine the packing density and moisture content of the silage simultaneously and eliminate the influence of the friction between the penetration shaft and silage. In this study, we not only design a compound sensor but also provide an alternative way to investigate the ensiling quality which would be useful for further silage research

In Situ Identification Method of Maize Stalk Width Based on Binocular Vision and Improved YOLOv8

ObjectiveThe width of maize stalks is an important indicator affecting the lodging resistance of maize. The measurement of maize stalk width has many problems, such as cumbersome manual collection process and large errors in the accuracy of automatic equipment collection and recognition, and it is of great application value to study a method for in-situ detection and high-precision identification of maize stalk width.MethodsThe ZED2i binocular camera was used and fixed in the field to obtain real-time pictures from the left and right sides of maize stalks together. The picture acquisition system was based on the NVIDIA Jetson TX2 NX development board, which could achieve timed shooting of both sides view of the maize by setting up the program. A total of maize original images were collected and a dataset was established. In order to observe more features in the target area from the image and provide assistance to improve model training generalization ability, the original images were processed by five processing methods: image saturation, brightness, contrast, sharpness and horizontal flipping, and the dataset was expanded to 3500 images. YOLOv8 was used as the original model for identifying maize stalks from a complex background. The coordinate attention (CA) attention mechanism can bring huge gains to downstream tasks on the basis of lightweight networks, so that the attention block can capture long-distance relationships in one direction while retaining spatial information in the other direction, so that the position information can be saved in the generated attention map to focus on the area of interest and help the network locate the target better and more accurately. By adding the CA module multiple times, the CA module was fused with the C2f module in the original Backbone, and the Bottleneck in the original C2f module was replaced by the CA module, and the C2fCA network module was redesigned. Replacing the loss function Efficient IoU Loss(EIoU) splits the loss term of the aspect ratio into the difference between the predicted width and height and the width and height of the minimum outer frame, which accelerated the convergence of the prediction box, improved the regression accuracy of the prediction box, and further improved the recognition accuracy of maize stalks. The binocular camera was then calibrated so that the left and right cameras were on the same three-dimensional plane. Then the three-dimensional reconstruction of maize stalks, and the matching of left and right cameras recognition frames was realized through the algorithm, first determine whether the detection number of recognition frames in the two images was equal, if not, re-enter the binocular image. If they were equal, continue to judge the coordinate information of the left and right images, the width and height of the bounding box, and determine whether the difference was less than the given Ta. If greater than the given Ta, the image was re-imported; If it was less than the given Ta, the confidence level of the recognition frame of the image was determined whether it was less than the given Tb. If greater than the given Tb, the image is re-imported; If it is less than the given Tb, it indicates that the recognition frame is the same maize identified in the left and right images. If the above conditions were met, the corresponding point matching in the binocular image was completed. After the three-dimensional reconstruction of the binocular image, the three-dimensional coordinates (Ax, Ay, Az) and (Bx, By, Bz) in the upper left and upper right corners of the recognition box under the world coordinate system were obtained, and the distance between the two points was the width of the maize stalk. Finally, a comparative analysis was conducted among the improved YOLOv8 model, the original YOLOv8 model, faster region convolutional neural networks (Faster R-CNN), and single shot multiBox detector (SSD)to verify the recognition accuracy and recognition accuracy of the model.Results and DiscussionsThe precision rate (P)、recall rate (R)、average accuracy mAP0.5、average accuracy mAP0.5:0.95 of the improved YOLOv8 model reached 96.8%、94.1%、96.6% and 77.0%. Compared with YOLOv7, increased by 1.3%、1.3%、1.0% and 11.6%, compared with YOLOv5, increased by 1.8%、2.1%、1.2% and 15.8%, compared with Faster R-CNN, increased by 31.1%、40.3%、46.2%、and 37.6%, and compared with SSD, increased by 20.6%、23.8%、20.9% and 20.1%, respectively. Respectively, and the linear regression coefficient of determination R2, root mean square error RMSE and mean absolute error MAE were 0.373, 0.265 cm and 0.244 cm, respectively. The method proposed in the research can meet the requirements of actual production for the measurement accuracy of maize stalk width.ConclusionsIn this study, the in-situ recognition method of maize stalk width based on the improved YOLOv8 model can realize the accurate in-situ identification of maize stalks, which solves the problems of time-consuming and laborious manual measurement and poor machine vision recognition accuracy, and provides a theoretical basis for practical production applications

Influencing factors of wide pulse pressure in an elderly Chinese population: A cross‐sectional study

Abstract Blood pressure and pulse pressure (PP) had their own characteristics in the elderly population. This cross‐sectional study including 5030 elderly participants was conducted to describe the distribution of blood pressure and wide PP in the elderly population and find influencing factors of wide PP. Wide PP was defined as PP equal to or more than 65 mmHg, and was classified three types as low systolic blood pressure (SBP) and low diastolic blood pressure (DBP) (LSLD), high SBP and low DBP (HSLD), and high SBP and high DBP (HSHD). Using multivariate logistic regression models to analyze the associations of demographic factors, health‐related factors and lifestyle factors with different wide PP types. The associations of lifestyles with wide PP by gender were estimated by subgroup analyses. Among 5030 elderly participants, 2727 (54.2%) participants had wide PP. Logistic regression models showed older age (OR = 2.48, 95%CI: 2.14‐2.88), female (OR = 1.31, 95%CI: 1.07‐1.60), not married (OR = 1.26, 95%CI: 1.07‐1.49), having chronic diseases (OR = 1.28, 95%CI: 1.09‐1.50), current alcohol drinker (OR = 1.29, 95%CI: 1.11‐1.50) were positively associated, and higher body height (OR = .78, 95%CI: .62‐.99), higher education level (OR = .60, 95%CI: .43‐.82), current smoker (OR = .79, 95%CI: .64‐.97) were negatively associated with wide PP. Among three different types of wide PP including LSLD, HSLD, HSHD, these factors had different effects. Subgroup analyses found that only among male, current smoker was negatively associated and current alcohol drinker was positively associated with wide PP

Association of walkability and NO2 with metabolic syndrome: A cohort study in China

Background: Epidemiological studies have reported an association between traffic-related pollution with risk of metabolic syndrome (MetS). However, evidence from prospective studies on the association of walkability and nitrogen dioxide (NO2) with MetS is still scarce. We, therefore, aimed to evaluate the association of long-term exposure to NO2 and walkability with hazards of incident MetS. Methods: A total of 17,965 participants without MetS diagnosed within one year at baseline were included in our study from a population-based prospective cohort in Yinzhou District, Ningbo, Zhejiang Province, China. Participants were followed up by the regional Health Information System (HIS) until December 15, 2021. MetS was defined based on the criteria of Chinese Diabetes Society (CDS2004). We used walkscore tools, calculating with amenity categories and decay functions, and spatial–temporal land-use regression (LUR) models to estimate walkability and NO2 concentrations. We used Cox proportional hazards regression models to examine the association of walkability and NO2 with hazards of MetS incidence reporting with hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Overall, we followed up 77,303 person-years and identified 4040 incident cases of MetS in the entire cohort. Higher walkability was inversely associated with incident MetS (HR = 0.94, 95 % CI: 0.91–0.99), whereas NO2 was positively associated with MetS incidence (HR = 1.07, 95 %CI: 1.00–1.15) per interquartile range increment in two-exposure models. Furthermore, we found a significant multiplicative interaction between walkability and NO2. Stronger associations were observed for NO2 and incident MetS among men, smokers, drinkers and participants who aged < 60 years and had higher levels of income. Conclusion: In summary, we found living in areas with lower walkability and higher concentrations of NO2 were associated with increased incidence of MetS. The beneficial effect of higher walkability may be attenuated by exposure to NO2

GhCYS2 governs the tolerance against cadmium stress by regulating cell viability and photosynthesis in cotton

Cysteine, an early sulfur-containing compound in plants, is of significant importance in sulfur metabolism. CYS encodes cysteine synthetase that further catalyzes cysteine synthesis. In this investigation, CYS genes, identified from genome-wide analysis of Gossypium hirsutum bioinformatically, led to the discovery of GhCYS2 as the pivotal gene responsible for Cd2+ response. The silencing of GhCYS2 through virus-induced gene silencing (VIGS) rendered plants highly susceptible to Cd2+ stress. Silencing GhCYS2 in plants resulted in diminished levels of cysteine and glutathione while leading to the accumulation of MDA and ROS within cells, thereby impeding the regular process of photosynthesis. Consequently, the stomatal aperture of leaves decreased, epidermal cells underwent distortion and deformation, intercellular connections are dramatically disrupted, and fissures manifested between cells. Ultimately, these detrimental effected culminating in plant wilting and a substantial reduction in biomass. The association established between Cd2+ and cysteine in this investigation offered a valuable reference point for further inquiry into the functional and regulatory mechanisms of cysteine synthesis genes

Additional file 1 of GhIMP10D, an inositol monophosphates family gene, enhances ascorbic acid and antioxidant enzyme activities to confer alkaline tolerance in Gossypium hirsutum L.

Additional file 1: Supplementary Table S1. Gene locus ID and their proposed names of all observed species and the gene characteristics in G. hirsutum. Supplementary Table S2. Duplicated gene pairs in 10 combinations (Ga-Ga, Ga-Gb, Ga-Gr, Gb-Gb, Gb-Gr, Gh-Gh, Gh-Ga, Gh-Gb, Gh-Gr and Gr-Gr). Supplementary Table S3. Non-synonymous (Ka) and synonymous (Ks) divergence values for Ga-Ga, Ga-Gb, Ga-Gr, Gb-Gb, Gb-Gr, Gh-Gh, Gh-Ga, Gh-Gb, Gh-Gr and Gr-Gr. Supplementary Table S4. Primer pairs used for this experiment