# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 4 Barriers to Incorporating Artificial Intelligence Technology in Smart Greenhouses
### Sensor Signal Failure and Communication Barriers  
The growing advocacy for the integration of AI in precision agriculture negates the technology-related challenges to commercial adoption. [PERSON] et al. established that interferences often compromised communication between sensor nodes. There were multiple sources of interferences for sensor signal transfer in greenhouses, including crop canopy, soil, and temperature [87; 164]. Similarly to [PERSON] et al. [87], [PERSON] et al. [78] observed that the accuracy of the soil moisture sensors was notably low in regions with high vegetative diversity. Sensor accuracy was impaired by different hydrological properties and geography, especially in real-world conditions dissimilar to the training datasets. On a positive note, most of the constraints identified by [PERSON] et al. [87] and [PERSON] et al. [78] have been addressed using AI technologies. In particular, [PERSON] et al. demonstrated the importance of fuzzy clustering techniques in the humidity prediction modules. The fuzzy logic controllers enabled the AI systems to model and predict the behavior of key greenhouse parameters. The system was appealing to the farmers because it was inexpensive and easy to use. A major concern was that these issues were often disregarded in research in favor of the mass transition to an intelligent greenhouse system.  
The risk of impaired transmission identified by [PERSON] et al. [87] may be resolved using the hardware configuration proposed by [PERSON] et al. [58]. The configuration featured Siap+Micros t055 pyranometers and Apogee SQ-515 Quantum sensors with a 1.2 mV sensitivity of 1.2 mV. The configuration proposed by [PERSON] et al. [58] bears semblance to [PERSON] et al., who deployed Apogee instruments to measure the photosynthetic photon flux density (PPFD) over the plant canopy in a greenhouse structure [14]. The observations made by [PERSON] et al. [58] and [PERSON] et al. [11] do not take into account the variable accuracy of the Apogee solar radiation sensors. According to [PERSON] et al., the Apogee sensors had a mean accuracy of \(\pm 5\%\)[172]. The accuracy of the sensor systems could be enhanced using the Remote Sensing Assisted Control System (RSCS), which offered multidimensional benefits, including the measurement of CO\({}_{2}\) emissions, soil moisture, irrigation control, and agricultural production capacities.  
New experimental data have confirmed that using plastic mulches with sustainable remote sensing and AI technology systems is practical. In particular, [PERSON] et al. [173] reported the successful use of Landsat-5 thematic mapper and hyperspectral airborne sensors to map plastic mulched farmlands and plastic-covered greenhouses. The data were, in turn, employed to identify regions where plastic mulches can be gradually replaced with biodegradable alternatives. The claims made by [PERSON] et al. [173] concerning the role of technology in mapping plastic mulches were in tandem with [PERSON] et al., who deployed remote-sensing aperture radar systems to map plastic mulches [174].