High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops

Research on Factors Affecting the Use of E-commerce Consumer Credit Services: A Study of Ant Check Later

This study uses “Ant Check Later”, the e-commerce consumer credit service of Alibaba, as the artifact and explores factors affecting its use. This study first summarized initiatives that Alibaba has launched to stimulate the use of “Ant Check Later”. Three factors, bonus, quota lifting, and scenario enrichment, were then distinguished from the initiatives using principal component analysis. These factors were anticipated to affect consumers’ intention to use the service. The research model was tested using 373 respondents collected from an online survey. Results indicate that bonus, quota lifting, and scenario enrichment are three predictors of consumers’ intention to continue using the service, and bonus and scenario enrichment positively affect non-users’ intention to use the service. This study found that scenario enrichment is the most important factor among the three factors in boosting consumers’ behavioral intention toward using the service. Keywords E-commerce consumer credit services, bonus, quota lifting, scenario enrichment, acceptance

Exploiting knowledge graph for multi-faceted conceptual modelling using GCN

The relevant information obtained from multiple sources usually contributes to one intricate phenomenon in the industrial processes. Data fusion of different sources usually leads to more expressive and informative information than that of each single data source. Integrated information has been widely used to model a multi-faceted conceptual phenomenon, which provides a comprehensive and versatile view of understanding of the process. However, the conventional approaches concatenate feature vectors to integrate different facets, not considering the semantic gaps between them. Meanwhile, knowledge graph (KG) receives considerable attention in recent years as it comprises rich relational information among elements. Thus, KG provides a promising way to fuse multiple data sources by bridging the semantic gaps, which can be exploited in the modelling of a multi-faceted phenomenon. Inspired by the advancement of KG, we proposed an approach based on KG and a machine learning algorithm for multi-faceted modelling. Firstly, a domain-specified ontology was built to eliminate the varying distance metrics across facet boundaries, and KGs were generated by populating the data surrounding a multi-faceted phenomenon into this ontology. Secondly, the KGs were fed into a graph convolutional neural network (GCN) to learn the node features and the graph structure for graph embedding simultaneously with the shared parameters. Lastly, with the aim of multi-faceted conceptual modelling, the features obtained from the GCN model were used as inputs for machine learning algorithms to learn the hidden patterns of KGs. An experimental study using real-world data from the cold rolling process was conducted to demonstrate the feasibility of the proposed model

Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects

Smart manufacturing (SM) confronts several challenges inherently suited to knowledge graphs (KGs) capabilities. The first key challenge lies in the synthesis of complex and varied data surrounding the manufacturing context, which demands advanced semantic analysis and inference capabilities. The second main limitation is the contextualization of manufacturing systems and the exploitation of manufacturing domain knowledge, which requires a dynamic and holistic representation of knowledge. The last major obstacle arises from the facilitation of intricate decision-making processes towards correlated manufacturing ecosystems, which benefit from interconnected data structures that KGs excel at organizing. However, the existing survey studies concentrated on distinct facets of SM and offered isolated insights into KG applications while overlooking the interconnections between various KG technologies and their application across multiple domains. What specific role KGs should play in SM towards the aforementioned challenges, how to effectively harness KGs for these challenges, and the essential topics and methodologies required to make KGs functional remain underexplored. To explore the potential of KGs in SM, this study adopts a systematic approach to investigate, evaluate, and analyse current research on KGs, identifying core advancements and their implications for future manufacturing practices. Firstly, cutting-edge developments in the challenge-driven roles of KGs and KG techniques are identified, from knowledge extraction and mining to techniques for KG construction and updates, further extending to KG embedding, fusion, and reasoning—central to driving SM ecosystems. Specifically, the KG technologies for SM are depicted holistically, emphasizing the interplay of diverse KG techniques with a comprehensive framework. Subsequently, this foundation outlines and discusses key application scenarios of KGs from engineering design to predictive maintenance, covering the main representative stages of the manufacturing life cycle. Lastly, this study explores the intricate interplay of the practical challenges and advantages of KGs in manufacturing systems, pointing to emerging research avenues