In today’s world of digitization, IoT plays a significant role. It has made eve-rything smart, e.g., smart cities smart healthcare, industrial automation, commercial), and even farming is not untouched by IoT. Due to which data generated by IoT devices will reach up to 180 zettabytes by 2025 as forecast by the International Data Corporation . To handle such enormous data, the edge computing approach has created innovation opportunities within the IoT ecosystem by applying cloud services to the network edge to reduce network latency and serve IoT applications in real-time. Edge computing has applications where Internet connectivity is poor or unreachable because, in this case, sensors are not able to communicate with the cloud efficiently. So in this paper, we have implemented a distributed edge computing platform for smart farming to increase/improve crop productivity in remote agri-land. Since edge computing nodes are resource-constrained, diverse, and distribut-ed in nature, edge applications require to be built as a set of smaller, interde-pendent modules. These tactics are in line with microservices architecture, so proposed smart farming is based on a microservices architecture that al-lows service distribution across discrete computing nodes in the IoT-edge-cloud architecture. Results show that by applying service distribution and lo-cal processing at the edge layer, we achieve an 89.85% decrease in the quan-tity of data migrated to the cloud server.