## 6 Analysis and Results
### Libellium Connector: Implementation and Observations
#### 6.2.1 ASCII Encoding
These algorithms could be employed to automatically classify data from various sensors and map it in real time to the required standard, thereby reducing the need to manually design specific connectors for each sensor family [109]. This approach could not only enhance operational efficiency but also lower maintenance costs and facilitate the expansion of the system to accommodate new devices. The future goal is to develop a wider variety of connectors to support the majority of commonly used sensors in the agriculture market and to integrate Large Language Model(LLM) algorithms to enhance the scalability of the proposed architecture [110; 111]. Furthermore, to enhance the system's completeness, future implementations may include support for communication with actuator devices. The second part of the OGC SensorThings standard provides a communication method for interfacing with actuators. Therefore, it will be the responsibility of the connector to receive information sent by users and \"translate\" it into the device's language, effectively performing the reverse operation of what has been outlined in this work. The integration of actuators allows the system to go beyond simple monitoring and reporting, providing automation that increases resource efficiency, reduces human labour, and optimizes conditions for healthy crops, for example, automating fertilization systems [112] and implementing smart deficit irrigation systems, where the aim is to reduce water wastage without compromising fruit production [113]. Finally, while the proposed architecture is scalable and cost-effective, potential challenges such as integrating legacy systems and ensuring data security must be addressed to promote adoption across diverse agricultural contexts. ### Impact on Precision Agriculture

The proposed architecture is a fresh approach to agricultural resource management that fosters productivity, sustainability, and resource efficiency. By the integration of various IoT devices in a modular and holistic system, the architecture enables precise, real-time monitoring of the most critical variables such as temperature of the soil, nitrogen concentration, leaf wetness, and climatic conditions. In this manner, natural resources such as water are utilized at optimal levels, while waste and inefficiency in irrigation are reduced. Moreover, simple modification and customisation of system parts owing to the use of open-source platforms such as FROST-Server and modular connectors facilitate the usage of the technology in small-sized and large-sized agricultural settings. The use of data-driven approaches ensures predictive crop care, increased output, and smaller environmental impact [114]. Therefore, the architecture will be able to support a more sustainable agriculture that is able to address climate change issues and limited resources and provide more productive but less wasteful farm production. ### Comparison with Other Solutions

Interoperability is probably the biggest challenge in precision agriculture, as farms will use a wide variety of sensors and devices from different manufacturers [115]. This creates environments where systems cannot communicate well with each other and integration and data management become impossible. The literature shows some IoT platforms, such as FIWARE [116], mySense [117] and Cayenne [118], as potential solutions to these problems. FIWARE is an open-source initiative to provide standardised interfaces for integrating heterogeneous devices. In contrast, mySense is an architecture aimed at integrating sensor data in agriculture, using LoRa technology. Meanwhile, mySense presents a 4-layers technology: sensor nodes, networks, cloud services, and front-end applications. Cayenne offers a simple solution for small- and medium-sized farms gradually moving towards the adoption of smart technologies. However, to the best of our knowledge, the solutions proposed in the literature did not delve into the integration of third-party commercial devices such as Libellium and Digital Matter, as is addressed in this work.