# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 1 Introduction  
Effective irrigation of agricultural land is a very significant issue, not only addressed by areas and countries with limited water supplies. To overcome challenges such as water scarcity and seasonal shortage, numerous irrigation systems have been designed to automate the process. Most of those systems are composed of three distinct parts: (a) the environmental sensors component; (b) the decision-state machine component where the control logic resides; and (c) the actuator component [1]. Modern irrigation systems with excellent performance maintain less water consumption, while ensuring maximum crop productivity. This critical performance point is in most cases a tradeoff between water supply and gross crop production. The determination of this optimal point is hard to indicate since it is affected by a number of static parameters such as field elevation, slope and cultivation as well as dynamic parameters such as micro-climate conditions, seasonal plant diseases, soil enrichment and fertilization et al. The already existent sensory-based irrigations systems include IoT actuators and sensors. The Koubachi Company (Zurich, Switzerland) offers a smart watering product that consists of a soil sensor and a central controller. It uses a self-learning watering algorithm with statically assigned weights [2]. [PERSON] and [PERSON] proposed an Arduino-based smart drip irrigation system that is continuously monitoring pH, temperature and moisture levels [3]. [PERSON] and [PERSON] proposed an automation of Irrigation System based on Wi-Fi Technology and IoT [4]. [PERSON] al. proposed an IoT precision irrigation system for greenhouses, which use a wireless sensor network [5]. The implementation of smart algorithms into IoT devices, equipped with environmental sensors, has been developed, by setting aside existing time-scheduled watering algorithms and algorithms based on static on/off thresholds. Such simple IoT algorithms could lead to the increase of production from 25 to 30% while reducing considerably water excess [6]. The implementation of fuzzy [7; 8] and neural network algorithms [9] to the irrigation process, also known as smart irrigation algorithms, has results according to human (Artificial Intelligent, A.I. algorithms)-driven decisions and not according to the calibration of static thresholds. However, such smart algorithms require a network infrastructure for cloud uploads, cloud storage and bi-directional data exchange between controllers and application services for post-processing evaluation of sensory measurements. No matter how promising the experimental results from such systems are, they still have significant complexity, scalability, interoperability issues, and big power consumption, all of which constitute expensive solutions for agricultural placement and present communication disruption problems. By taking into consideration the irrigation plans, which are determined by the requirements of the agricultural land and are assisted by the IoT devices for precise agriculture, new adaptive, sensory-assisted decision algorithms and smart irrigations systems using A.I. logic have been implemented. A smart irrigation system is defined as a system that controls field irrigation without user interference. Smart irrigation systems are divided into two main categories: (i) the weather sensory adaptive; and (ii) and the soil moisture-soil sensory adaptive. In the weather-based decision processes, irrigation decisions are based on feedback taken from a weather-monitoring system. Such a system collects data from sensors that measure the rain level, humidity, temperature, wind and solar radiation, located in the agriculture field of interest [10]. Weather feedback irrigation systems can monitor severe weather conditions in order to avoid irrigation in cases of extreme meteorological conditions [10]. Moreover, it should measure the rainfall and stop the irrigation when it starts raining.