# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 4 Barriers to Incorporating Artificial Intelligence Technology in Smart Greenhouses
### Impact of Demographics and Socioeconomic Factors on AI Technology Adoption in Agriculture  
The shortage of young, progressive EU farmers raises legitimate concerns regarding the actualization of Eurozone agricultural innovation policies, and by extension, the ability to sustain higher food demands [67]. The issues raised in the European Economic and Social Committee's report [67] were partly in line with [PERSON] and [PERSON]'s research in Germany, where age predicted technology adoption [161]. For example, smartphone use and access decreased with age among the German farmers sampled. Similarly to [PERSON] and [PERSON] [161], [PERSON] [71] noted that less educated and older farmers were less inclined to adopt precision farming technologies. However, the observations by [PERSON] and [PERSON] [161] concerning the rate of technology acceptance in agriculture were not aligned with the European Economic and Social Committee's report [67]. Most of the sampled German farmers were young (mean age = 33 years), and there was more than an 80% assimilation of smart farming technologies among old and young farmers. The contrasting observations from [PERSON] and [PERSON] and the European Economic and Social Committee's report raise fundamental questions on whether demographics were a barrier to adopting AI on smart farms across Europe and the world. In contrast to the European Economic and Social Committee's report [67] and [PERSON] and [PERSON] [161], [PERSON] [71] claimed multiple externalities aided and impeded smart farming technology adoption; these variables included farm size, financial status, technology factors, information resources, social, economic issues (age, experience, and education), and farm size. The latter argument provides a more representative picture of the dynamics that led to higher or lower technology adoption that is more applicable to developing and developed countries in the North and the South [137]. Further research on the role of demographics, farm size, financial status, technology factors, and information resources was necessary, considering achieving and sustaining exponential growth in AI technology adoption in agriculture is a prerequisite for tangible global impact on crop yields, pest and disease management, and optimization of agricultural resources [168]. Despite the overwhelming evidence in support of AI, particularly robotics in harvesting, critics argue that AI cannot entirely \"replace/replicate humans. Machines do not have the multi-perspective skills of humans, as they are typically programmed to perform specific tasks in constrained conditions\" [69] (p. 9). The issues raised by [69] do not consider recent milestones with bio-inspired algorithms that improved the precision and reliability of AI and ML infrastructure in agriculture [169]. The use of bio-inspired hybrid algorithms for weed optimization was a case in point [14; 105; 135]. The role of AI algorithms highlighted by [14; 105; 135] was corroborated by [PERSON] et al. [131], who used image processing algorithms to improve the ground-based accuracy of a robotic system. The selection of advanced algorithms, including Hierarchy/Hybrid Particle Swarm Optimization (HPSO), Grey Wolf Optimizer (GWO), Evolutionary Algorithm (EA), Direct Artificial Bee Colony (DABC), and Hybrid Genetic Algorithm with Particle Swarm Optimization (GA-PSO) among others involves a tradeoff between computational speed, path planning, fitness, root means squared, and mean absolute errors [85]. For example, the ANN algorithm had lower MAE and RSME values compared to the GEP testing and training evolutionary algorithm (see Table 4). However, the ANN required higher computational resources [14; 170]. On a positive note, the technical constraints were short-term and should not be considered a critical impediment to the adoption of AI systems. Beyond the technically related barriers to the widespread adoption of AI systems in smart farming, there were emerging concerns about the impact of farmer demographics on the adoption of AI systems.