# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 5 Future Perspectives  
The critical review of the current body of knowledge supporting the integration of AI systems in agriculture yielded new insights and perspectives on how best to enhance the efficiency of smart greenhouses. However, certain issues should be addressed in upcoming studies. For example, the current body of knowledge did not establish a consensus on whether automated agricultural machinery manufacturers and commercial farms should transition from satellite triangulation to light imaging, detection and ranging (LIDAR), optical and electro-optical cameras [131], or Satellite-Differential GPS (S-DGPS). The mixed observations by [PERSON] et al. [131], [PERSON] [130], and [PERSON] and [PERSON] [132] warrant further research inquiry considering there were different technical requirementsfor open space and indoor (greenhouse) robotic systems. Future research on LIDAR and optical camera systems for open space and greenhouse robotic tractors would provide better insights into the cost-benefits of available technologies to guide commercial adoption. Empirical and farm-based data are important, considering [PERSON] and other companies were commercializing new autonomous tractor models fitted with optical cameras [130,132]. Moreover, there was a growing acceptance of soft robotic systems for fruit and vegetable harvesting and pesticide application. Beyond systems for robotic structures, current data on the suitability of different variants of bio-inspired algorithms (swarm intelligence, genetic and evolutionary) for pesticide application, and robotic harvesting, automated regulation of irrigation, soil treatment, fertilizer application, and seeding are specific to the Global North [105,135]. Future research should explore the suitability of bio-inspired algorithms to support the automation of greenhouses in developing countries. Isolated case studies of milestones in developing countries support the recommendation. For example, ERNN systems augmented temperature and humidity measurements in Ethiopia's greenhouses [170]. However, there were gaps in the knowledge, which may impede decision-making among stakeholders in the future. Future research should investigate whether the LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, or RFID communication technologies were best suited for specific bio-inspired algorithms and the influence of local conditions and variables on performance and accuracy. At present, researchers are focusing on different algorithms to refine algorithm performance. For example, most studies focused on solar radiation intensity, transpiration, and CO\({}_{2}\) concentration to predict crop yields, even though the approach yielded inaccurate results [18]. The standardization of input variables would improve accuracy and performance. Upcoming studies should help establish whether intelligent IPM was a sustainable alternative to traditional chemical pesticides and biopesticides, considering pesticide resistance and the need to adopt sustainable agricultural practices. Additionally, there is a growing demand for technological solutions to address climate change and global food insecurity, which urges the integration of AI technologies like light imaging, detection and ranging 1128 (LIDAR), optical and electro-optical cameras [131] or satellite-differential GPS (S-DGPS). Apart from the technological aspect, there is also a need to develop agricultural production policies that favor the funding of these technologies and their integration to agricultural production. Such policies can include setting aside grant funds for institutions and companies involved in their development. Such policies should also include solving the cost barriers that have consistently been highlighted in previous research. Most studies have also demonstrated the potential application of AI in large scale agricultural production.