  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.
