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Towards Smart Microfarming in an Urban Computing Continuum

Marla Grunewald, Mounir Bensalem, Jasenka Dizdarević, Admela Jukan

TL;DR

This paper proposes and builds a system architecture for a plant recommendation system that uses machine learning at the edge to find, from a pool of given plants, the most suitable ones for a given microfarm using monitored soil values obtained from IoT sensor devices.

Abstract

Microfarming and urban computing have evolved as two distinct sustainability pillars of urban living today. In this paper, we combine these two concepts, while majorly extending them jointly towards novel concepts of smart microfarming and urban computing continuum. Smart microfarming is proposed with applications of artificial intelligence (AI) in microfarming, while an urban computing continuum is proposed as a major extension of the concept towards an efficient Internet of Things (IoT) -edge-cloud continuum. We propose and build a system architecture for a plant recommendation system that uses machine learning (ML) at the edge to find, from a pool of given plants, the most suitable ones for a given microfarm using monitored soil values obtained from IoT sensor devices. Moreover, we propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge system, due to its unlicensed nature and potential for open source implementations. Finally, we propose to integrate open source and less constrained application protocol solutions, such as Advanced Message Queuing Protocol (AMQP) and Hypertext Transport Protocol (HTTP) protocols, for storing the data in the cloud. An experimental setup is used to evaluate and analyze the performance and reliability of the data collection procedure and the quality of the recommendation solution. Furthermore, collaborative filtering is used for the completion of an incomplete information about soils and plants. Finally, various ML algorithms are applied to identify and recommend the optimal plan for a specific microfarm in an urban area.

Towards Smart Microfarming in an Urban Computing Continuum

TL;DR

This paper proposes and builds a system architecture for a plant recommendation system that uses machine learning at the edge to find, from a pool of given plants, the most suitable ones for a given microfarm using monitored soil values obtained from IoT sensor devices.

Abstract

Microfarming and urban computing have evolved as two distinct sustainability pillars of urban living today. In this paper, we combine these two concepts, while majorly extending them jointly towards novel concepts of smart microfarming and urban computing continuum. Smart microfarming is proposed with applications of artificial intelligence (AI) in microfarming, while an urban computing continuum is proposed as a major extension of the concept towards an efficient Internet of Things (IoT) -edge-cloud continuum. We propose and build a system architecture for a plant recommendation system that uses machine learning (ML) at the edge to find, from a pool of given plants, the most suitable ones for a given microfarm using monitored soil values obtained from IoT sensor devices. Moreover, we propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge system, due to its unlicensed nature and potential for open source implementations. Finally, we propose to integrate open source and less constrained application protocol solutions, such as Advanced Message Queuing Protocol (AMQP) and Hypertext Transport Protocol (HTTP) protocols, for storing the data in the cloud. An experimental setup is used to evaluate and analyze the performance and reliability of the data collection procedure and the quality of the recommendation solution. Furthermore, collaborative filtering is used for the completion of an incomplete information about soils and plants. Finally, various ML algorithms are applied to identify and recommend the optimal plan for a specific microfarm in an urban area.
Paper Structure (14 sections, 1 equation, 5 figures, 3 tables)

This paper contains 14 sections, 1 equation, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Description
  4. Reference Architecture
  5. Workflow engineering
  6. ML pipeline for plant recommendation
  7. Data collection
  8. Pre-processing
  9. Recommendation approaches
  10. Experimental and Simulation Results
  11. LoRa performance
  12. Collaborative filtering performance
  13. Plant recommendation
  14. Conclusion

Figures (5)

  • Figure 1: Urban Computing Continuum for Smart Microfarming.
  • Figure 2: Engineering the microfarming data collection workflow
  • Figure 3: Engineering the microfarming recommendation workflow.
  • Figure 4: Confusion matrix with different data sparsity and rating distribution.
  • Figure 5: Prediction performance vs number of soils