Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

Liu, Xiaoxue; Bai, Xuesong; Wang, Longhe; Bingyuan, Ren; Lu, Shuhan; Li, Lin

doi:10.1109/access.2019.2915987

Cited by 48 publications

(24 citation statements)

References 56 publications

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“…Next rainbow concatenation in R-SSD is integrated. Pooling and de convolution are utilized simultaneously to integrate the context and fuse features of the feature pyramid at the backbone of the SSD for highest small disease object detection in [16].…”

Section: Methodology: Deep Neural Networkmentioning

confidence: 99%

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Identification of Sugarcane Foliar Diseases Methods and Datasets

Daphal,

Koli

2020

IJEAT

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Agriculture is the major part of the Indian economy as it provides key support to social and economic development of the country. Sugarcane is the leading cash crop in various states of India which has larger share in the net agriculture produce. Recently, researches have highlighted the impact of different disease on various plants. Estimated loss is much severe for the sugarcane crop via foliar diseases. Foliar diseases like rust, eye spot, mosaic and banded chlorosis may hamper the overall productivity of sugarcane and sugar recovery rate (RR). Early prediction of these diseases may limit the losses in terms of produce net benefits. This paper addresses the concerns, types of the foliar disease and researches undertaken to overcome the problems related to the diseases. Morphological characters of these diseases may help in identifying the representative features and to use them for the optimum classification. Currently the use of deep neural networks (DNN) is encouraged for the classification. DNN demands the huge and accurate databases. Intuitively the use and important methods used in database creation for disease diagnostic system (DDS) has been highlighted in the paper. Modifications made to the Convolutional Neural Network architecture have suggested the improved performance in terms of recognition accuracy (RA) and lesser recognition time.

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Section: Methodology: Deep Neural Networkmentioning

confidence: 99%

“…System was significant and used variants of convolutional network [15]. Similar knowledge based methods for crop disease detection is thoroughly mentioned in [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Sugarcane Foliar Diseases Methods and Datasets

Daphal,

Koli

2020

IJEAT

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“…Based on these open KGs, various industry KGs that are applicable to different industries (such as finance, agriculture, geography, meteorology, medicine and education) have also been constructed. These include the Gene Ontology [25] and Traditional Chinese Medicine KG [26] for biomedicine, Crop Diseases and Insect Pests [27] for agriculture, FOAF for social relations [28] and KGs for social networks [29], GeoNames Ontology and GeoKG for geospatial big data [30], [31], OSM Semantic Network [32]- [34], transportation KG [35], [36], KGs for multimedia conferencing process management [37], etc.…”

Section: B Knowledge Graph 1) Overview Of Knowledge Graphmentioning

confidence: 99%

Construction of Space Object Situation Information Service Based on Knowledge Graph

Lan

et al. 2020

IEEE Access

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To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multielement knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.

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“…The increasing use of ontologies is also seen in agriculture (e.g., [4,6,[17][18][19][20][21][22][23][24][25][26]), where they are used for various purposes, such as agriculture knowledge sharing across farmers around the world and in different languages [18,25,[27][28][29], creating semantic interoperability of agricultural systems [4,22,30,31], and supporting farmer decisions [32] by providing automatic knowledge inference. This is not surprising, given that agriculture is a knowledge-centric field that covers many areas of expertise and many world-wide used practices and technologies.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Evaluating Agricultural Ontologies

2021

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An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.

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Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

Cited by 48 publications

References 56 publications

Identification of Sugarcane Foliar Diseases Methods and Datasets

Identification of Sugarcane Foliar Diseases Methods and Datasets

Construction of Space Object Situation Information Service Based on Knowledge Graph

A Framework for Evaluating Agricultural Ontologies

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