# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Current AI Technologies for Agriculture
#### 2.2.2 Water and Fertilizer Use Efficiency and Crop Yields with AI Systems  
The success of the GOSSYM (a combined simulation model for plant growth and decision) aid for cotton crop management mode influenced the iterative development of other AI systems, including the HortiMED-AI-powered Decision Support Systems (DSSs) [74]. On a positive note, the barriers to the transition from labor- and input-intensive agriculture to knowledge-based farming would be eliminated over time; this is evident with the recent innovations in AI systems for smart/precision agriculture. In 2022, [PERSON] reported the successful development of the HortiMED-AI-powered decision support systems (DSSs) that facilitated autonomous greenhouse control using sensors, \"multilayer hierarchical control architecture,\" smart algorithms, \"hybrid modeling combining well-known mechanistic models with AI techniques,\" IoT, and machine learning (p. 1) [74]. The HortiMED-DSS was capable of facilitating cost-effective automation of greenhouse heating, ventilation, and fertigation, and providing expert services relating to nutrient, crop, and climatic conditions (fertilizer and water application rates and timelines). The observations made by [74] regarding the benefits of automating greenhouse were in tandem with [PERSON] et al. [75]; precision irrigation and fertilizer application led to higher nitrogen use and water use efficiency, and in turn, a lower risk of pests and diseases in greenhouse cucumbers in China's Yangling region. The observations made by [PERSON] et al. [75] about AI-mediated water-use efficiency corroborated the study by [PERSON] et al., which noted that AI technologies provided water to crops within their demand ranges [76]. From a theoretical point of view, [PERSON] et al. [75] and [PERSON] et al. [76] advanced a simplistic view of sensor operation, which did not align with [PERSON] et al. [77]. In the latter case, [PERSON] et al. [77] argued that the accuracy of the soil moisture sensors was dependent on hardware components and environmental degradation of the sensor components. In line with [PERSON] et al. [77], there were cases when the dielectric soil moisture sensors failed to accurately measure periodic changes in the soil moisture levels; this had a negative domino effect on demand irrigation. The faulty measurements were linked to either corrosion or hardware malfunction. The outcome reported by [PERSON] et al. [77] negated the impact of soil type on sensor malfunction--an issue that was extensively investigated by [PERSON] et al. [78]. The study confirmed that the \"resistive sensor operates defectively in sandy loam and clay loam soils owing to low bulk density and high organic matter\" (p. 5) Emerging research recommended the adoption of capacitive soil moisture sensors to counteract the limitations of the resistive sensors. From the researcher's perspective, the risk of corrosion was disproportionately higher, considering the dielectric sensors were buried in situ and the soils were humid [77]. The risk of sensor malfunction highlights the potential drawbacks and limits of demand-drivenagriculture. Despite the mounting evidence on the risk of a sensor malfunction, the technology issues are often disregarded due to cost considerations and the anticipated cost savings. [PERSON] et al. [75] reported a 17.5% higher cucumber yield following the introduction of AI-based irrigation, while the partial factor productivity of applied nitrogen increased by 29%. Similarly, with AI-based systems, [PERSON] et al. [79] reported a 17% improvement in wheat grain yields. Considering the outcomes were statistically significant (\(p<0.005\)), AI-based irrigation and fertilizer application yielded tangible benefits compared to traditional drip irrigation and manual soil fertilization. The positive outcomes reported by [PERSON] et al. [75] were consistent with the [PERSON] et al. case study in Saudi Arabia, where superior partial factor productivity of applied nitrogen with ANN (Artificial neural network) models was established [79].