M-DCocoa: M-Agriculture Expert System for Diagnosing Cocoa Plant Diseases

Yusof, Munirah Mohd; Rosli, Nur Fazliyana; Othman, Muhaini; Mohamed, Ramizi; Abdullah, Mohd Sohaimi

doi:10.1007/978-3-319-72550-5_35

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…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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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: 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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“…In India, rice is accountable for 70% of overall crop production and 93% overall production in terms of grain yield. For this reason, early detection of diseases in paddy plants is of national importance to ensure the productivity of staple in India [1]. Identification and classification of pests and plant diseases remain the main challenge faced in the field of agriculture.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

Almasoud¹,

Eisa²,

Duhayyim³

et al. 2022

Computers, Materials &Amp; Continua

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In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner. In addition, EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions. The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix (GLCM) and Inception-based deep features to derive the features. Finally, Salp Swarm Optimization with Fuzzy Support Vector Machine (FSVM) model is utilized for classification. In order to validate the enhanced outcomes of EDLFM-RPD technique, a series of simulations was conducted. The results were assessed under different measures. The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

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“…Most of the problems can be reduced by providing some technical features, detecting diseases on time and taking precautions can increase productivity and therefore the need to seek experts may not be necessary (El-kazzaz et al, 2015). Recently, the identification and classification of plant diseases has become one of the important research topics in agriculture (Yusof et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Çeltik yanıklığı hastalığının görüntü işleme teknikleri kullanılarak tespit edilmesi

SOYDAN¹,

Taner²

2021

Anadolu Journal of Agricultural Sciences

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M-DCocoa: M-Agriculture Expert System for Diagnosing Cocoa Plant Diseases

Cited by 22 publications

References 6 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

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

Çeltik yanıklığı hastalığının görüntü işleme teknikleri kullanılarak tespit edilmesi

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