Abstract
In agriculture, the experiences of farmers are very valuable but difficult to replace and passing on. The lack of working power is also a serious problem for many agriculture countries. For planting organic crops, irrigation is one of the most critical steps but also a very labor intensive work. This paper provides a machine learning-based precise and smart irrigation system with LoRa P2P networks to automatically and seamlessly learn the irrigation experiences from expert farmers for greenhouse organic crops. The proposed system will firstly calculate the amount of water for each irrigation based on the trained irrigation model combined with the environment data, such as air temperature/humidity, soil temperate/humidity, light intensity, etc., and then irrigate the crops automatically via the long-distance and low-power wireless LoRa P2P network. The MAC protocol of standard LoRaWAN is Aloha based (random access) and may not be suitable for real-time automatically control. We implement the automatic irrigation system with LoRa P2P network which is a master-slave and TDM-based MAC protocol. Experimental results show that the proposed smart and precise irrigation system is very suitable for modern green house-based agriculture.