Design of Intelligent Detection Platform for Wine Grape Pests and Diseases in Ningxia

In order to reduce the impact of pests and diseases on the yield and quality of Ningxia wine grapes and to improve the efficiency and intelligence of detection, this paper designs an intelligent detection platform for pests and diseases. The optimal underlying network is selected by comparing the recognition accuracy of both MobileNet V2 and YOLOX_s networks trained on the Public Dataset. Based on this network, the effect of adding attention mechanism and replacing loss function on recognition effect is investigated by permutation in the Custom Dataset, resulting in the improved network YOLOX_s + CBAM. The improved network was trained on the Overall Dataset, and finally a recognition model capable of identifying nine types of pests was obtained, with a recognition accuracy of 93.35% in the validation set, an improvement of 1.35% over the original network. The recognition model is deployed on the Web side and Raspberry Pi to achieve independent detection functions; the channel between the two platforms is built through Ngrok, and remote interconnection is achieved through VNC desktop. Users can choose to upload local images on the Web side for detection, handheld Raspberry Pi for field detection, or Raspberry Pi and Web interconnection for remote detection

Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD

The detection of Lingwu long jujubes in a natural environment is of great significance for robotic picking. Therefore, a lightweight network of target detection based on the SSD (single shot multi-box detector) is presented to meet the requirements of a low computational complexity and enhanced precision. Traditional object detection methods need to load pre-trained weights, cannot change the network structure, and are limited by equipment resource conditions. This study proposes a lightweight SSD object detection method that can achieve a high detection accuracy without loading pre-trained weights and replace the Peleenet network with VGG16 as the trunk, which can acquire additional inputs from all of the previous layers and provide itself characteristic maps to all of the following layers. The coordinate attention module and global attention mechanism are added in the dense block, which boost models to more accurately locate and identify objects of interest. The Inceptionv2 module has been replaced in the first three additional layers of the SSD structure, so the multi-scale structure can enhance the capacity of the model to retrieve the characteristic messages. The output of each additional level is appended to the export of the sub-level through convolution and pooling operations in order to realize the integration of the image feature messages between the various levels. A dataset containing images of the Lingwu long jujubes was generated and augmented using pre-processing techniques such as noise reinforcement, light variation, and image spinning. To compare the performance of the modified SSD model to the original model, a number of experiments were conducted. The results indicate that the mAP (mean average precision) of the modified SSD algorithm for object inspection is 97.32%, the speed of detection is 41.15 fps, and the parameters are compressed to 30.37% of the original networks for the same Lingwu long jujubes datasets without loading pre-trained weights. The improved SSD target detection algorithm realizes a reduction in complexity, which is available for the lightweight adoption to a mobile platform and it provides references for the visual detection of robotic picking

Analysis and Experiment of Cutting Mechanical Parameters for Caragana korshinskii (C.k.) Branches

In order to investigate the cutting mechanical characteristics of Caragana korshinskii (C.k.) branches and explore the optimal combination of cutting parameters to support the subsequent equipment development, this paper explores the relationship between branch diameter D, average cutting speed v, wedge angle β, slip cutting angle α, cutting height h, cutting gap t, moisture content M and peak cutting force by using a homemade swing-cut branch cutting test bench with peak cutting force of branches as the target value under unsupported and supported cutting methods, respectively, through single-factor tests. Based on the single-factor test, v, β, α and t were selected as the test factors, and a multi-factor test was conducted with the peak cutting force as the target. Test result: The best combination of unsupported cutting in the range of multi-factor test is v for 3.315 m·s−1, β for 20°, α for 20°, when the peak cutting force is 95.690 N. Supported cutting multi-factor test range to get the best combination of v for 3.36 m·s−1, β for 20°, α for 20°, t for 1.38 mm, when the peak cutting force is 53.082 N. The errors of the predicted peak cutting force and the measured peak cutting force of the obtained model were 1.3% and 3.9%, respectively, which prove that the cutting parameters were optimized reliably. This research can provide a theoretical basis for subsequent development the C.k. harvesting equipment

Multi-objective Optimization of Peak Cutting Force and Cutting Energy Consumption in Cutting of Caragana korshinskii Branches

Caragana korshinskii (C.K.) flat stubble residue is an abundant biomass energy source in China. Because branch cutting is closely related to the harvesting of forest biomass, it is practical for forestry production and ecological development to investigate the effects of cutting parameters on the peak cutting force and cutting energy consumption of C.K. branches. In this study, the effect of cutting parameters on the peak cutting force and cutting power consumption of branches was investigated by single-factor and multi-factor tests using an independently developed reciprocating cutting test bench, and an optimization model was established. The interaction term of average cutting speed and tool cutting edge inclination angle significantly affected the peak cutting force, while the interaction term of cutting clearance and wedge angle had a significant effect on the cutting energy consumption. The optimal combination of cutting parameters was an average cutting speed of 0.5 m/s, cutting clearance of 1.4 mm, wedge angle of 25°, and tool cutting edge inclination angle of 20°. With this combination of parameters, the corresponding peak cutting force was 644.38 N, and the cutting energy consumption was 5.90 J, which was less than 5% relative error between each performance index and the theoretical optimized value

The Development and Validation of a Rapid Assessment Tool of Primary Care in China

Introduction. With Chinese health care reform increasingly emphasizing the importance of primary care, the need for a tool to evaluate primary care performance and service delivery is clear. This study presents a methodology for a rapid assessment of primary care organizations and service delivery in China. Methods. The study translated and adapted the Primary Care Assessment Tool-Adult Edition (PCAT-AE) into a Chinese version to measure core dimensions of primary care, namely, first contact, continuity, comprehensiveness, and coordination. A cross-sectional survey was conducted to assess the validity and reliability of the Chinese Rapid Primary Care Assessment Tool (CR-PCAT). Eight community health centers in Guangdong province have been selected to participate in the survey. Results. A total of 1465 effective samples were included for data analysis. Eight items were eliminated following principal component analysis and reliability testing. The principal component analysis extracted five multiple-item scales (first contact utilization, first contact accessibility, ongoing care, comprehensiveness, and coordination). The tests of scaling assumptions were basically met. Conclusion. The standard psychometric evaluation indicates that the scales have achieved relatively good reliability and validity. The CR-PCAT provides a rapid and reliable measure of four core dimensions of primary care, which could be applied in various scenarios

Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field

In order to accurately obtain the distribution of large-field grape-planting sites and their planting information in complex environments, the unmanned aerial vehicle (UAV) multispectral image semantic segmentation model based on improved DeepLabV3+ is used to solve the problem that large-field grapes in complex environments are affected by factors such as scattered planting sites and complex background environment of planting sites, which makes the identification of planting areas less accurate and more difficult to manage. In this paper, firstly, the standard deviation (SD) and interband correlation of UAV multispectral images were calculated to obtain the best band combinations for large-field grape images, and five preferred texture features and two preferred vegetation indices were screened using color space transformation and grayscale coevolution matrix. Then, supervised classification methods, such as maximum likelihood (ML), random forest (RF), and support vector machine (SVM), unsupervised classification methods, such as the Iterative Self-organizing Data Analysis Techniques Algorithm (ISO DATA) model and an improved DeepLabV3+ model, are used to evaluate the accuracy of each model in combination with the field visual translation results to obtain the best classification model. Finally, the effectiveness of the classification features on the best model is verified. The results showed that among the four machine learning methods, SVM obtained the best overall classification accuracy of the model; the DeepLabV3+ deep learning scheme based on spectral information + texture + vegetation index + digital surface model (DSM) obtained the best accuracy of overall accuracy (OA) and frequency weight intersection over union (FW-IOU) of 87.48% and 83.23%, respectively, and the grape plantation area relative error of extraction was 1.9%. This collection scheme provides a research basis for accurate interpretation of the planting structure of large-field grapes

DataSheet_1_Delineation of estuarine ecological corridors using the MaxEnt model to protect marine fishery biodiversity.docx

Ecological corridors (ECs) are important management tools to protect biodiversity by linking fragile habitats, especially for highly mobile organisms. ECs in terrestrial landscapes work as passages on land or in water. However, the significance of ECs to migratory species in estuaries has not been well elucidated. Based on annual fishery investigation in the Yangtze estuary and their dominance index rank, three of the top five species, including Larimochthys polyactis, Coilia mystus, and Gobiidae, exhibited absolute dominance in spring during the past 5 years. The temporal and spatial density variance of C. mystus supported its short-distance migration pattern. Redundancy analysis and the MaxEnt model predicted optimum habitats for C. mystus. C. mystus larvae survival was significantly related to salinity, total nitrogen, pH, reactive silicate, dissolved oxygen, surface water temperature, and chlorophyll-a in May and to salinity, surface water temperature, permanganate index, suspended particles, total nitrogen, and total phosphorus in August. The MaxEnt model predicted a broader longitudinal distribution range from offshore to the upstream freshwater area but narrower latitudinal distribution in the southern branch in May than in August. Finally, we delineated migratory corridors connecting optimum habitats for C. mystus using the least-cost route method. Optimum habitats close to the coastlines in the south branch might play a significant role in maintaining population or community connectivity in the Yangtze estuary. Our findings provide a perspective and method to quantify and facilitate the harmonious development of socioeconomy and fishery biodiversity conservation.</p

An integrated high-throughput strategy enables the discovery of multifunctional ionic liquids for sustainable chemical processes

Development of new chemical processes with simplified reaction systems and work-up procedures is a challenging task. Although ionic liquids are a class of potential multifunctional compounds to simplify traditional chemical processes, their rational design is difficult due to complex interactions. In this work, a proof-of-concept strategy has been proposed to achieve an integration of high-throughput preparation of ionic liquids and in situ screening of their reaction-promoting performance in 96-well plates. The integrated approach then enables a facile identification of optimal ionic liquids from a 400-ionic liquid candidate pool to act as the solvent, the catalyst and the separating assistant, simultaneously, for carbonylazide cycloaddition reactions. Merits of the ionic liquids-based processes have been demonstrated not only in the convenient and efficient synthesis of 1,2,3-triazolyl compounds but also in the discovery of a new reaction for the chemical post-modification of free peptides.</p