Research on an Agricultural Knowledge Fusion Method for Big Data

Xie, Naiming; Wang, Wensheng; Ma, Bingxian; Zhang, Xuefu; Sun, Wei; Guo, Fenglei

doi:10.5334/dsj-2015-007

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…With recent efforts to synthesize and make available large datasets, the need for new systematic approaches to address data gaps and uncertainties will become increasingly important for the agricultural and natural sciences (Xie et al, 2015). This is particularly true for LAI.…”

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confidence: 99%

Allometric Method to Estimate Leaf Area Index for Row Crops

et al. 2017

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Core Ideas A new allometric method was developed to estimate LAI for row crops. Good agreement resulted for four crops over multiple seasons. Best agreement resulted using GDD, plant population, and canopy height. Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric methods (i.e., exploiting relationships between LAI and more easily measured plant variables), but these depend on other measurements not widely available, and have limited transferability to different seasons. A new allometric method using a log normal function was developed to calculate LAI. Input variables were normalized cumulative growing degree days (CGDD), canopy height (CH), and plant population (PP), which were usually more widely available in crop production datasets. Destructive LAI measurements were obtained over multiple growing seasons for corn (Zea mays L.), cotton (Gossypium hirsutum L.), sorghum (Sorghum bicolor L.), and soybean [Glycine max (L.) Merr.] at USDA‐ARS, Bushland, TX. Log normal functions were calibrated to LAI measurements from a single season of each crop, and tested using independent LAI measurements from all remaining crop seasons. For all crops, discrepancies between calculated and measured LAI resulted in coefficients of determination from 0.23 to 0.85, model indices of agreement from 0.52 to 0.84, root mean square errors from 0.76 to 1.4, mean absolute errors from 0.57 to 1.2, and mean bias errors from ‐0.46 to 0.60. The new allometric method can mitigate missing or sparse LAI data, which will enhance the value of large ecological datasets.

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confidence: 99%

Allometric Method to Estimate Leaf Area Index for Row Crops

et al. 2017

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“…Further, several definitions on what is the notion of ontology have been proposed. According to [31], these include three main definitions, we call informal definitions, since no formal definition exists [32]:…”

Section: Ontologiesmentioning

confidence: 99%

Enriching Domain Ontologies with Knowledge-based Semantics

Sy¹

2020

SJC

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Both association rules and ontologies are domain-based knowledge. However, the first is unexpected discovered knowledge from databases, while the second is a priori knowledge. In this paper, based on a generic meta-schema as common referential and theoretical foundation, we show how a set of computed and pruned association rules can be useful for enriching a domain ontology. To this end, the meta-schema is scanned with the itemsets appearing in each association rule. Then, according to the formal links between the concepts or the attributes involved, a semantic-based check constraint is built. As a result, the ontology and the database are continuously tuned with new semantics.

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“…The Wellmo support website can easily handle interactions with hundreds of users but as we scale up to first thousands, then tens of thousands and moving on to hundreds of thousands of users for which we want to process data in (near) real time we are going to need the Tieto cloud service platform (figure 5), that can work with input of sensor data from fitness devices, smart mobile phones, digital health devices, etc., with data from all types of databases and from third party software systems and can produce reports for dashboards on smart mobile phones (that can be, for instance, a Wellmo format), reports for smart watches, tablets or laptops, etc. Advanced analytics tools that will work in an environment of heterogeneous and not well structured data sources [6] are needed to build data fusion [29] with further extensions to information and knowledge fusion [28]. The fusion is a…”

Section: Enhanced Digital Wellness Servicesmentioning

confidence: 99%

Digital Wellness Services for Young Elderly: a Missed Opportunity for Mobile Services

Carlsson

Walden

2016

J. theor. appl. electron. commer. res.

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The ageing population of Europe is a concern for political decision makers and visions on how to deal with the issues are being debated. The issues raised concern elderly people, the age group 75-90 years, but not much thought is given the young elderly-the age groups 60-75 years-as the serious age-related problems are not visible among them. Nevertheless, pro-active preventive action programs among the young elderly could significantly reduce the problems society faces when people become elderly. We will address the issues with the ageing population from the perspective of digital wellness services that are designed, produced and offered over omnivore platforms on smart mobile phones and backed up by analytics tools over cloud services. The aim is to keep the young elderly healthy, active and independent when they reach the 75+ age group. In this research the digital wellness services are proposed as effective interventions to build wellness routines. The design of wellness services need to be done through co-creation with the young elderly, not for them.

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Research on an Agricultural Knowledge Fusion Method for Big Data

Cited by 12 publications

References 11 publications

Allometric Method to Estimate Leaf Area Index for Row Crops

Allometric Method to Estimate Leaf Area Index for Row Crops

Enriching Domain Ontologies with Knowledge-based Semantics

Digital Wellness Services for Young Elderly: a Missed Opportunity for Mobile Services

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