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 in microfarming, while an urban computing continuum is proposed as a major extension of the concept towards an efficient IoT-edge-cloud continuum. We propose and build 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. Moreover, we propose to integrate long-distance 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 AMQP and 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.

翻译：微农业与城市计算已发展成为当今城市生活的两大独立可持续性支柱。本文融合了这两个概念，并将其共同拓展至智能微农业与城市计算连续体的新范式。智能微农业的提出聚焦人工智能在微农业中的应用，而城市计算连续体则作为该概念向高效物联网-边缘-云连续体的重要延伸。我们设计并构建了一套植物推荐系统架构，该系统利用边缘端的机器学习技术，基于物联网传感器获取的土壤监测数据，从给定植物库中筛选出最适合特定微农场的品种。此外，鉴于LoRa通信的非授权频谱特性及开源实现潜力，我们提出集成远距离LoRa通信方案以实现物联网至边缘系统的数据传输。最后，我们建议采用AMQP与HTTP等开源且约束较少的应用层协议解决方案，将数据存储至云端。通过实验装置评估分析了数据采集流程的性能可靠性及推荐系统的解决方案质量。进一步采用协同过滤技术补全土壤与植物信息的不完整数据。最终应用多种机器学习算法，为城市特定微农场识别并推荐最优种植方案。