# Smart Hydroponic Cultivation System for Lettuce
## 1 Introduction
The fifth layer, or analytics layer, utilizes various algorithms to process real-time data, enabling predictive analysis of various crop aspects. Finally, the sixth layer, or user experience layer, is where farmers use applications, such as social media, to share and disseminate agricultural knowledge. Next, a review of recent studies (2020-2025) on the development and implementation of smart agricultural systems focused on small-scaled hydroponics is presented. The research explores significant advancements in technologies such as IoT and artificial intelligence (AI) applied to agriculture. In [18], a small-scale IoT-based aeroponic system (90 cm \(\times\) 60 cm \(\times\) 40 cm) is proposed for cultivating water spinach (_Ipomoea aquatic_ Forssk). In the physical layer of the system, a DHT-11 sensor is used to measure air temperature and relative humidity (RH) in the root chamber and control mechanisms including a Peltier cell for cooling, fans and mist generators. A Wemos D1 Mini microcontroller makes decisions based on internal air temperature and RH values. The network layer utilizes the microcontroller's built-in Wi-Fi connectivity to transmit real-time data to the service layer, where the ThingSpeak platform [19] is used for visualization and recording of the information. The user experience layer is implemented via QR codes, enabling manual activation and deactivation of control mechanisms. The system was tested for 16 days, demonstrating stable environmental conditions in the root chamber. In [20], an IoT aquaponics system architecture is proposed to monitor environmental variables for both plants and fish. For the physical layer, control mechanisms such as water pumps are used along with various sensors to monitor pH, electrical conductivity (EC), water level, water temperature, \(CO_{2}\) and ammonia. A NodeMCU microcontroller (Makerfabs, Shenzhen, China) handles decision-making and also functions as a gateway, transmitting data via Message Queuing Telemetry Transport (MQTT) to the service layer. The user experience layer is implemented through a web application for remote monitoring. In [21], the design and development of an automated hydroponics system is presented. In the physical layer, multiple sensors are integrated to measure variables such as air temperature, RH, substrate moisture and water level in tanks. Various control mechanisms are also employed, such as Light Emitting Diode (LED) lamps, a refrigeration system, ventilation equipment and pumping devices for the nutrient solution. An ATmega microcontroller is used for decision-making. The information collected by the monitoring system is sent via Zigbee protocol based on IEEE 802.15.4 [22] to the service layer for storage. In the user experience layer, the operator can visualize and control the connected devices through an interface developed in Laboratory Virtual Instrument Engineering Workbench (LabVIEW). In [23], the development and implementation of a smart hydroponic system were carried out. The physical layer consists of several sensors for measuring environmental variables such as air temperature and light intensity, as well as nutrient solution variables such as pH, nitrogen and phosphorus and for capturing plant images. Control mechanisms are included for irrigation and lighting systems. A Raspberry Pi (Raspberry Pi Foundation, Cambridge, United Kingdom) serves as the decision-making central control unit and transmits data to the network layer. In the analysis layer, artificial intelligence is implemented using a deep learning convolutional neural network (DLCNN) prediction model for data analysis. These models estimate the nutrient values required by the plants based on reference values and environmental conditions. Additionally, in the user experience layer, a DLCNN classification system is used to send alerts via a mobile application. In [24], a smart monitoring and control system for hydroponics applied to lettuce cultivation was designed.