Knowledge-Based System for Crop Pests and Diseases Recognition

Rodríguez-García, Miguel Ángel; García‐Sánchez, Francisco; Valencia‐García, Rafael

doi:10.3390/electronics10080905

Cited by 23 publications

(13 citation statements)

References 84 publications

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“…According to Hogan [31], there are two general techniques for knowledge accumulation in knowledge graphs: deductive (rule and logical inference) and inductive (machine learning and network analysis). In the field of plant pathology, most previous studies have used logical inference and ontology coverage on certain plants, such as cocoa plants [17], rice [18], and several other plants [19][20][21]. While research using inductive techniques and covering a wide range of organisms is relatively rare.…”

Section: Literature Reviewmentioning

confidence: 99%

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Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus

Katili,

Yeni Herdiyeni,

Hardhienata

2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order. Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector

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Section: Literature Reviewmentioning

confidence: 99%

“…Previous research has used ontologies to address plant pathology problems [17][18][19][20][21]. However, research that applies network analysis to GloBI graphs to identify insect vectors has not been widely carried out.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus

Katili,

Yeni Herdiyeni,

Hardhienata

2024

J. Inf. Syst. Eng. Bus. Intell.

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“…Moreover, procedural knowledge structuring, information tracking, and sequencing are a significant part of structuring knowledge from iTexts which cannot be solved only with entity relationships. In [2], an agricultural knowledge base or framework has been proposed which helps to identify pests and diseases that affect a crop. An automatic ontology population tool has been developed.…”

Section: Knowledge-based and Ontology-based Approachesmentioning

confidence: 99%

Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

2021

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An organized knowledge structure or knowledge base plays a vital role in retaining knowledge where data are processed and organized so that machines can understand. Instructive text (iText) consists of a set of instructions to accomplish a task or operation. Hence, iText includes a group of texts having a title or name of the task or operation and step-by-step instructions on how to accomplish the task. In the case of iText, storing only entities and their relationships with other entities does not always provide a solution for capturing knowledge from iTexts as it consists of parameters and attributes of different entities and their action based on different operations or procedures and the values differ for every individual operation or procedure for the same entity. There is a research gap in iTexts that created limitations to learn about different operations, capture human experience and dynamically update knowledge for every individual operation or instruction. This research presents a knowledge base for capturing and retaining knowledge from iTexts existing in operational documents. From each iTexts, small pieces of knowledge are extracted and represented as nodes linked to one another in the form of a knowledge network called the human experience semantic network (HESN). HESN is the crucial component of our proposed knowledge base. The knowledge base also consists of domain knowledge having different classified terms and key phrases of the specific domain.

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“…IoT sensors, robotics and image processing exploited by Convolutional Neural Networks (CNN) were combined in [4] for plant disease diagnosis. Natural language processing was applied in [5] for crop pests and disease recognition. A review of machine vision approaches used in the identification of common invertebrates (butterflies, snails, etc.)…”

Section: Introductionmentioning

confidence: 99%

Mobile Plant Disease Classifier, Trained with a Small Number of Images by the End User

et al. 2022

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Mobile applications that can be used for the training and classification of plant diseases are described in this paper. Professional agronomists can select the species and their diseases that are supported by the developed tool and follow an automatic training procedure using a small number of indicative photographs. The employed classification method is based on features that represent distinct aspects of the sick plant such as, for example, the color level distribution in the regions of interest. These features are extracted from photographs that display a plant part such as a leaf or a fruit. Multiple reference ranges are determined for each feature during training. When a new photograph is analyzed, its feature values are compared with the reference ranges, and different grades are assigned depending on whether a feature value falls within a range or not. The new photograph is classified as the disease with the highest grade. Ten tomato diseases are used as a case study, and the applications are trained with 40–100 segmented and normalized photographs for each disease. An accuracy between 93.4% and 96.1% is experimentally measured in this case. An additional dataset of pear disease photographs that are not segmented or normalized is also tested with an average accuracy of 95%.

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Knowledge-Based System for Crop Pests and Diseases Recognition

Cited by 23 publications

References 84 publications

Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus

Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus

Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

Mobile Plant Disease Classifier, Trained with a Small Number of Images by the End User

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