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A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)

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  • Bikram Pratim Bhuyan

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248006, India
    LISV Laboratory, University of Paris Saclay, 10-12 Avenue of Europe, 78140 Velizy, France
    These authors contributed equally to this work.)

  • Ravi Tomar

    (Persistent Systems, Pune 411016, India
    These authors contributed equally to this work.)

  • Amar Ramdane Cherif

    (LISV Laboratory, University of Paris Saclay, 10-12 Avenue of Europe, 78140 Velizy, France
    These authors contributed equally to this work.)

Abstract

Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge representation and reasoning” is devoted to the representation of information about the environment in a way where a computer system can utilise it to accomplish difficult problems. This research is an extensive survey of the knowledge representation techniques used in smart agriculture, and specifically in the urban agricultural domain. Relevant articles on the knowledge base are extracted from the retrieved set to study the fulfillment of the criteria of the system. Various interesting findings were observed after the review. Spatial–temporal characteristics were rarely approached. A generalised representation technique to include all domains in agriculture is another issue. Finally, proper validation technique is found to be missing in such an ontology.

Suggested Citation

  • Bikram Pratim Bhuyan & Ravi Tomar & Amar Ramdane Cherif, 2022. "A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)," Sustainability, MDPI, vol. 14(22), pages 1-36, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15249-:d:975366
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    Cited by:

    1. Bikram Pratim Bhuyan & Ravi Tomar & T. P. Singh & Amar Ramdane Cherif, 2022. "Crop Type Prediction: A Statistical and Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    2. Olga Mapanje & Selma Karuaihe & Charles Machethe & Mao Amis, 2023. "Financing Sustainable Agriculture in Sub-Saharan Africa: A Review of the Role of Financial Technologies," Sustainability, MDPI, vol. 15(5), pages 1-20, March.

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