# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 6 Conclusions  
The findings drawn from this research had practical consequences for commercial agriculture, considering a large body of knowledge focused on applying AI and IoT in traditional applications, and the emphasis on the benefits of the technology negated critical concerns about energy demand, sensor failure, accuracy, and long-term use. The findings broadened current knowledge on the cost benefits of sensors, robotic systems, algorithms, renewable energy systems for greenhouses, and support infrastructure. For example, users of soil and humidity sensors paired with fuzzy clustering techniques in the humidity prediction modules should be conscious of the fact that the accuracy of the measurements may be impaired by corrosion or hardware malfunction. Additionally, the new generation of new agricultural robotic systems largely depends on replacing GPS systems with LIDAR and optical camera systems, given the former exhibited poor object recognition. The performance of LIDAR systems was enhanced with image processing algorithms.  
The commercial readiness of AI technologies was demonstrated by John Deere company, Blue River Technology, Autonomous Tractor Corp, and other companies that had developed intelligence systems for monitoring crop and soil health, weed control, and self-driving agricultural tractors. Despite the commendable milestones achieved so far, there were multiple barriers to the use of the technology. The barriers included the cost of AI technology, variable performance of the algorithm in real-world conditions vis-a-vis training, the financial status of the farmers, lack of information resources, aging, and lack of prior experience with technology. Despite the shortcomings, adopting AI systems should be encouraged, given they improved crop yields, water use, and fertilizer use efficiency, and reduced the effects of pesticide resistance through intelligent IPM. Moreover, bio-inspired algorithms improved energy management by integrating photovoltaics and dynamic pricing and autonomous communication between the greenhouse system and EPG supply. The emerging climate change risks further reinforce the case for smart greenhouses powered by AI systems.  
In summary, most AI applications in agricultural production are in the development phase because they have not fully matured for commercialization purposes. The few AI solutions that have so far reached maturity and have been rolled out for commercial use despite challenges (e.g., accuracy) include the use of drones, sensors, and robots for harvesting crop yields. Although these technologies have been applied commercially, the challenge of accuracy and sustainability persists. For example, underground sensor hardware can be damaged by moisture and other unpredictable environmental conditions, leading to inaccurate data, which can easily mislead decisions in commercial farms, leading to huge losses. Therefore, although they have reached the commercial maturity, they still need further development to optimize their effectiveness in solving the identified problems and challenges. Furthermore, considering that many studies are currently being undertaken on this topic, it is also imperative to perform a similar mapping of the literature every three years to determine the current state of the art from time to time, because technological advancements are fast and imminent. Finally, more research is needed on overcoming the challenges of integrating AI applications into farm input in the Global South, which must include solving the cost barriers and knowledge and skills gaps.  
The author declares no conflict of interest.