## 2 Related Works  
Extensive research was conducted on the LoRa network and communication technologies. Foremost, it is identified that Low-Power Wide-Area (LPWAN) technologies have become popular worldwide [5]; such as LTE-M [6], SigFox, Narrow Band (NB)-IoT [7] and long-range LoRa [8]. The latter two being the ones that dominate wireless communications. Comparing these two technologies, it was identified that LoRa has advantages in terms of battery life, its capacity and cost; while NB-IoT offers benefits in terms of service quality (QoS), latency, reliability and range [9; 10]. It was also pointed out that despite the extensive research that has been conducted so far on existing LPWAN technologies, there are still challenges to be addressed that can be of great assistance to the scientific and academic community. Regarding the use of LoRa in agriculture, [11] proposes the development of a mobile gateway device with low-power wide-area networks (LoRaWAN), used to increase the productivity and efficiency of greenhouses. The results showed that this approach helps current agricultural processes, due to the low cost and accuracy of humidity and temperature measurements. On the other hand, in [12], a system was developed to transmit uninterrupted images taken from a camera in a static environment through LoRa, the purpose of which was to reduce the amount of data transmitted while maintaining the quality of the image and service. Research on agricultural monitoring with LoRa networks was also conducted. In [13], a soil environment monitoring system based on a radio frequency identification (RFID) sensor and LoRa is presented to conduct long-term and low-cost monitoring. In addition, in [14], a long-range, low-power IoT network was recommended to monitor soil moisture; using LoRa as the communication interface, which uses the 868 MHz ISM band for signal transmission. On the other hand, a model called AgriPrediction [15] was presented, which combines a short and medium-range wireless network system with a prediction engine to proactively predict changes in crops, thus notifying the farmer of corrective actions. Finally, studies on the state-of-the-art and the current situation in agriculture, IoT and the LoRa network [16; 17; 18] were reviewed. These studies represent a great starting point to arrive at intelligent agriculture. Other studies referring to the use of IoT in agriculture were identified in [19]. They developed an object detection model for the monitoring and control of weeds in grasslandsin the state of California. After a series of tests, the model showed an average of 94% accuracy in image detection, which is better than other models identified in the literature. On the other hand, in [20], a software framework based on a fuzzy logic system for the evaluation and cleaning of pastures is proposed to be designed. Via this framework, it is possible to measure the density of weeds and empty spots through images and score the state of pasture productivity. In addition, it is possible to produce 2D weed density maps, which provide a better view of the pastures. Finally, this field of study identified a Microservices software framework to implement automatic functions in the IoT-Fog-Cloud ecosystem, which will enable the development of intelligent decision-making systems based on the IoT context [21]. Another important field is the IoT/sensor networks operational optimization. The design, implementation and evaluation of an algo-handover rhythm for wireless sensor networks (WSN) are presented where different tests were performed, and it was identified that the proposed design can reduce the energy consumption by several orders of magnitude compared to existing handover algorithms for WSNs [22]. In this same field of study in [23], an Energy Efficient and Fault-Tolerant Hierarchical Clustering Algorithm for Wireless Sensor Networks (WSN) is proposed, which the authors named (FEHCA). The simulation results are encouraging as they enable better decision making on sensing data.