# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### AI Systems for Integrated Pest Management  
Despite the concerns raised about the prohibitive costs, the proponents of AI argue that the long-term benefits outweigh the short-term costs [84; 85]. The reliable use of AI systems to predict rice production was a case in point [85]. Beyond the prediction of crop production, AI systems facilitated the development of intelligent IPM systems capable of detecting early infestation of white flies (_Trialeurodes vaporarium_ and _Bemisia argentifolii_) and thrips (_Thrips tabaci_, _Frankliniella intonsa_, _Thrips tabaiensis_, and _Thrips tabaci_) [57]. The observations made by [PERSON] et al. [57] reinforce earlier observations made by [PERSON] et al. [14] on the suitability of deep learning systems in the management of pests and diseases. From a commercial perspective, the findings reported by [PERSON] et al. [57] and [PERSON] et al. [14] were compelling, but inadequate for catalyzing a paradigm shift in the market; this view was reinforced by the fact that the existing stock of AI systems for pest identification and management focused on insect pheromone identification [86]. The pheromone-centric ANN and wireless sensor network (WSN) technology cannot control all insect types, considering there is a limited number of commercially available pheromones for IPM [86].  
Despite the reservations reported by [PERSON] et al. [86], the ANN-WSN system for pest identification accurately categorized harmful and beneficial insects; these data guided the calculation of the volume of pesticides for IPM. The limits of WSN technology highlighted by [86] were in tandem with [87], who noted that variable monitoring ranges, complex typologies, and environmental conditions compromised the operation of WSN systems. However, recent R&D evidence suggests otherwise. The shortcomings of WSN identified by [86; 87] negate recent milestones achieved by [PERSON] et al. [88] with wireless sensors and actuators networks (WSAN). WSAN is an upgrade of WSN developed to resolve most of the limitations of WSN using actuators for better monitoring and control.  
The advances made using WSAN have not been accompanied by greater market adoption, owing to the newness of the technology; most studies still focus on WSN technologies for smart greenhouses [19; 89]. Beyond the concerns about the pace of market adoption, the body of knowledge, including [PERSON] et al. [57], [PERSON] et al. [86], and [PERSON] et al. [14], was informed by small-scale experiments under controlled conditions. Real-life applications of AI systems (IRIS scout robots and robot scouts) in identifying pests and diseases in agriculture yielded false positive data after analyzing pheromone trap images and powdery mildew [90]. Since the latter findings were based on market data, the IRIS scout robots and robot scouts were not ready for market adoption. Inaccurate assessment of pests and disease infestation using AI and ML (machine learning) image analysis techniques would result in the excessive application of pesticides. Further research and development efforts were necessary to minimize false positive rates.