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.

翻译：微农业与城市计算已发展成为当今城市生活的两大可持续性支柱。本文融合了这两个概念，并主要将其共同拓展为智能微农业与城市计算连续体的新颖理念。智能微农业的提出着眼于人工智能（AI）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（IoT）-边缘-云连续体的重要延伸。我们设计并构建了一个植物推荐系统的体系架构，该系统在边缘端利用机器学习（ML）技术，基于从物联网传感器设备获取的土壤监测数据，从给定植物池中为特定微农场筛选最适宜的植物。此外，鉴于其免许可特性及开源实现的潜力，我们提议集成远距离LoRa通信方案，用于将数据从物联网设备传输至边缘系统。最后，我们建议采用开源且约束较少的应用层协议解决方案（如高级消息队列协议AMQP和超文本传输协议HTTP）将数据存储至云端。通过实验装置，我们对数据收集流程的性能与可靠性以及推荐解决方案的质量进行了评估与分析。进一步地，采用协同过滤技术以补全土壤与植物信息的不完整部分。最终，通过应用多种机器学习算法，为城市区域的特定微农场识别并推荐最优种植方案。