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Taking the households of urban and rural residents in Zhejiang Province as the research object in 2020, an indicator system for rural development and urbanization is constructed from the four aspects of ecological environment, public services, grassroots governance and quality of life, using entropy method and Coupling Coordination Degree Model, Theil index and other methods to summarize the urban-rural development experience and problems of different counties and cities in Zhejiang Province, and explore the urban-rural coupling mode and implementation path. The results show that: ①Based on the analysis of different spatial scales, the urbanization and rural have regional imbalances in development, with obvious gradient changes from east to west and little difference between sub-regions. About 80% of the overall difference in urban-rural development in Zhejiang comes from differences within sub-regions, and another 60% comes from the internal differences between southwestern Zhejiang and southern Zhejiang. At the county level, the urbanization and rural development index presents a spatial pattern of "high in the northeast and low in the southwest". ②Based on the analysis of the degree of urban- rural coupling and coordination in Zhejiang Province, four urban-rural coupling modes are summarized: independent parallel coupling, urban-rural partial benefit interactive coupling, urban-rural symmetrical reciprocal coupling, and urban-rural integration symbiosis coupling. ③ Based on the rural revitalization strategy, the implementation path summarized from ecological livability, public services, grassroots governance and quality of life is characterized by ecological integration, green development, spatial integration, shared development, governance integration, good rural governance, factor integration, and common prosperity. [ABSTRACT FROM AUTHOR]

Cotton bud is one of the most important cash crops with many uses, such as the textile and cotton fabric raw material. However, manual cotton topping cannot fully meet the large-scale production in recent years, due to the efficiency and labor costs. Intelligent mechanical topping can be expected to serve as an inevitable trend during cotton topping. There is a high demand to accurately identify the cotton top bud under the complex environment (such as light and shadow), particularly for the various shape characteristics of cotton top core in the process of cotton cap removal. In this study the accurate recognition was proposed to locate the cotton top bud in the complex environment using an improved YOLOv5s. A dataset was also constructed to contain 3103 cotton top buds with different morphological characteristics under a complex environment. Three categories were divided: single, multi-plant and screened top bud under different occlusion areas, weather and light conditions. Systematic training and analysis were then performed on the data. An improved YOLOv5s model was proposed to reduce the error and leakage detection for the sufficient features of small targets. Multi-scale target features were also effectively extracted using only three detection heads in the original YOLOv5s. At the same time, the ideal extraction of the improved YOLOv5s was obtained with the deepening of the network layer. The object detection and CPP-CBAM attention mechanism were first added to the original structure, in order to improve the shallow and deep feature fusion rate. Secondly, the regression of prediction frame was enhanced to avoid the information loss of the real frame. The loss function SIOU boundary frame regression was added to further accelerate the prediction frame regression speed for the detection accuracy. The SIOU loss function was introduced by the vector angle between the real and predicted frame including angle, distance, and shape loss function. As such, the aspect ratio between the predicted and real frame was considered in the CIOU loss function with the direction between them in the SIOU. The high accuracy of recognition rate was obtained on the top bud, resulting in the less degrading into an IOU loss function. In addition, an accurate identification was performed on the cotton top bud that was seriously obscured by the leaves. The results show that the average accuracy (mAP) reached 92.8% for the recognition of cotton top bud by the improved model. Furthermore, the average accuracy was improved by 2.1, 3.3, 2, and 2.4 percentage points, respectively, compared with Fast R-CNN, YOLOv3, YOLOv5s, and YOLOv6. The improved detection model can be expected to accurately and efficiently identify the cotton top bud under a complex environment in real time. The small size of the model parameters and the high identification speed were suitable for the migration deployment of cotton topping machinery, and the operation of the laser topping robot. The finding can also provide technical and theoretical support to the subsequent operation of cotton laser topping machines, particularly for the mechanized real-time topping. [ABSTRACT FROM AUTHOR]

Safflower silk is one of the most important cash crops in the medical treatment and dye. The manual picking of safflower silk cannot fully meet the large-scale production at present. The mechanized harvesting can be expected to improve the safflower harvesting efficiency and labor cost-saving in the industrial planting. The complex environmental factors have made great difficulty to accurately identify and locate the safflower during mechanical picking, including the natural environment (such as the light and shelter), and the safflower characteristics (such as the small and dense target, as well as the different maturity). In this study, an improved YOLOv7 model was proposed to rapidly and accurately locate the safflower recognition in the complex environment. 1500 safflower images were established to divide into three types of samples, silk, bulb, and decay. The small target was found in the datasets with uneven sample size, especially the data of decay. The safflower sample dataset was produced to build the complex environment data for the real picking. Firstly, the Swin Transformer attention mechanism was added to the YOLOv7 network model, in order to improve the detection accuracy of the model for each classification sample, and the ability of the backbone network, especially to extract the small target features. Secondly, the Focal Loss function of the multi-classification was improved in the recognition rate of unbalanced samples under multi-class tasks, particularly for the target confidence loss and classification loss. The attenuation parameters γ was adjusted to balance the sample. The attenuation parameters were determined after experimental verification. Finally, the safflower detection network model was designed to meet the real-time and accurate detection requirements under the complex environment. The test results show that the best performance of the model was achieved in the attenuation parameters of 1.0, where the position of Swin Transformer was the layer 50. The average precision of the improved model reached 88.5% in each category sample, which was 7.0 percentage points higher than before. The average detection accuracy of the unbalanced category sample decay was 15.9 percentage points higher. Compared with the Faster RCNN model, the detection speed increased by three times, while the average accuracy increased by 9.7 percentage points; Compared with the Deformable DETR model, the detection speed increased by about 5 times, and the average accuracy increased by 16.5 percentage points. The improved model performed the best, in terms of the detection efficiency, detection speed, and model size. The improved model can also be expected to accurately detect the safflower, indicating the smaller parameters and the faster recognition speed suitable for the migration deployment on the safflower picking machinery. The finding can provide the technical support for the mechanized picking in real time. In the future research, the balance between species can be promoted to expand the number of samples from the data level in the harvesting machinery. [ABSTRACT FROM AUTHOR]

Xinmei is one of the European varieties with "paradise medicine fruit", because of its rich medicinal and health care and beauty functions. Today, Xinmei have been widely planted in Kashgar and Yili in Xinjiang Uygur autonomous region, China. Picking robots have also been used to harvest Xinmei in recent years. However, rapid and accurate visual detection is still challenging in the complex environment, such as overlap and occlusion. A large number of Xinmei fruits are blocked by leaves and trunks, due to the small target of Xinmei fruit and the dense branches and leaves. The key feature information of Xinmei cannot be detected for the expensive and high economic value. In addition, the more fruits overlapped, the larger the overlapping area among fruits was. In this study, a detection model was proposed for Xinmei in a complex environment using SFF-YOLOv5s. The dataset of Xinmei was constructed in the real orchard environment. The advanced YOLOv5s model was adopted as the network base. Firstly, the Coordinate Attention mechanism CA (Coordinate Attention) was introduced in the C3 module of the Backbone network, in order to extract the key feature information when the Xinmei was blocked by leaves. The number of parameters was reduced to embed on mobile devices. Secondly, the weighted bidirectional feature pyramid network was introduced into the Neck layer, in order to enhance the fusion among different feature layers of the model. The recognition of the model was promoted on the mutually occluding fruit. SIoU loss function was also used to replace the CIoU loss function in the original model, in order to accelerate the convergence speed for the high accuracy of the model. The test results showed that better performance was achieved, where the accuracy of the SFF-YOLOv5s model was 93.4%, the recall rate was 92.9%, the mean average precision (mAP) was 97.7%, the model weight was only 13.6MB, and the average detection time of a single image was 12.1ms. After the CA attention mechanism was added to the C3 module, the average accuracy of the improved model increased by 0.2 percentage points, while the number of parameters was reduced from 7.02 to 6.41M. With the weighted bidirectional feature pyramid network, the average accuracy of the model reached 97.6%, which was 0.5 percentage points higher than that of the original YOLOv5s. When SIoU was used as the loss function, the accuracy of the model was improved by 2 percentage points, compared with the original. Compared with the Faster R-CNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv7, and YOLOv8s models, the average accuracy (mAP) was improved by 3.6, 6.8, 13.1, 0.6, 0.4 and 0.5 percentage points, respectively. The lowest computation and weight were achieved with the high detection speed. Therefore, the optimal performance of the SFF-YOLOv5s model can fully meet the requirements of real-time detection of Xinmei in the complex orchard environment. The finding can provide technical support to the visual perception of xinmei in picking robots. [ABSTRACT FROM AUTHOR]

The experiment was to explore the feasibility of using the extractive liquid sediment (SXBS) produced in pharmaceutical process of Shengxuebao mixture as a feed additive. The research used UPLC-TOF-MS and other technologies to analyze the small molecule chemical composition and nutritional composition of SXBS, and SXBS was added into diet of New Zealand rabbits for 60 d. The effect of SXBS on growth performance, nutrient apparent digestibility, slaughter performance and intestinal flora of New Zealand rabbits were investigated. The feeding safety of SXBS was reflected by blood routine, blood biochemical analysis and histopathological observation. The results showed that SXBS was rich in nutrients such as carbohydrates, crude fiber, crude protein and crude fat, and contained a lot of active ingredients such as flavonoids, amino acids, terpenes, polyphenols and saponins. SXBS had no effect on growth performance of New Zealand rabbits. SXBS could significantly regulate the bacterial and fungal flora structure of New Zealand rabbits' gut, up-regulate linoleic acid, propionic acid, taurine, galactose and other metabolic functions related to the digestion and absorption of intestinal carbohydrates, and increase the abundance of Zygosaccharomyces ruberii. SXBS had no obvious toxic effect on New Zealand rabbits within 60 d, and the safety was good. The study indicates that SXBS can be used in rabbit breeding as a rabbit feed additive for regulating intestinal microecology. [ABSTRACT FROM AUTHOR]