# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 3 Digital AI Models for Energy Management in Greenhouses
### Alternative Energy Saving Measures--LoRa Transmission, PCOA-Lighting Management, PCMs, and LEDs  
Drawing from earlier case study data, the pilot experiments may not be practical for regions with diverse climatic conditions and unique energy requirements. The above concerns partly contributed to the diversification of AI approaches for energy management. In the meantime, the researcher posits that it would be prudent for smallholder farmers to make incremental changes for better energy management; these may include selecting connectivity technologies with lower energy requirements. For example, a comparison of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID technologies established the following. Leading connectivity technologies for AI systems with higher transmission ranges (20-100 m) had higher energy input requirements; Wi-Fi is a case in point. In contrast, LoRa had very low energy requirements and high transmission ranges (<30 km) [148]. The claims made by [PERSON] et al. [148] on the low energy requirements were in tandem with [PERSON] et al. [84], who noted that the system's typology reduced the energy demand while enhancing data transmission over long distances. Beyond the selection of communication channels, [PERSON] et al. [138] reported 54% to 83% energy savings in incandescent and fluorescent lamps after incorporating a parallel particle swarm algorithm for optimal location of lighting. The case for LoRa networks in agriculture made by [PERSON] et al. [148] and [PERSON] et al. [84] aligned with the research of [PERSON] et al. [149] on the market integration of LoRa technology in greenhouses. In one case, the LoRa transmission module was used to transfer data collected from sensors to the Raspberry Pi (single-board computer). In contrast to [PERSON] et al. [149], [PERSON] [150] observed that the accuracy of the Raspberry Pi system was higher when paired with Canon camera systems to improve the resolution of the images detected. After integrating the two systems, each plant had a resolution of 0.006 pixels. On the downside, farm-based data show that high transmission ranges and low energy requirements of LoRa did not automatically translate to greater commercial adoption in relation to -Fi, Bluetooth, ZigBee, mobile, and RFID. For example, [PERSON] et al. [151] preferred fuzzy-guided RFID-based communication channels for smart greenhouses featuring DHT22/ DS18B20 sensors for temperature and humidity regulation. In contrast to [PERSON] et al. [149], [PERSON] et al. [148], and [PERSON] et al. [84], [PERSON] et al. [152] and [PERSON] et al. [153] recommended alternative methods for managing energy resources, including the use of phase change materials for energy conservation and agricultural residue to generate biofuels and energy. However, the application of PCMs recommended by [PERSON] et al. [152] represents but a tiny fraction of the potential applications in smart farming. For example, [PERSON] et al. [154] noted that PCMs were appropriate materials for cold chain logistics [154]. However, not all scholars support the use of PCMs in greenhouses and related applications due to the high risk of leakage during the phase change process [155]. The researcher supports the use of PCMs, considering they can be coupled with solar systems for maximum energy saving. On the downside, incremental changes, such as selecting less energy-intensive communication systems, cannot suffice; they must be paired with other interventions, including the adoption of renewable energy systems in greenhouses [156,157]. Solar and geothermal energy are the most viable renewable energy systems [158]. Even though the transition to renewable energy was widely encouraged, the reliability of the energy source was not guaranteed, especially in regions with intermittent solar radiation [159].