# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Robotics and Algorithms for Precision Agriculture and Smart Greenhouses  
Recently, commercial farms have deployed robotic systems to selectively harvest fragile fruits and vegetables such as broccoli and tomatoes [91]. The findings by [91] were in agreement with [PERSON] et al. [92], who documented the increased use of robotic systems to sort tomatoes based on color, size, shape, and mass and to identify all defects during the harvesting process. The case for AI robotic systems in smart farming advanced by [PERSON] et al. [91] was corroborated by [PERSON] et al. [93] and [PERSON] et al. [94], who highlighted the benefits of soft robotic grippers in fruit and vegetable harvesting. In contrast to [PERSON] et al. [93] and [PERSON] et al. [94], [PERSON] et al. [95] argued that AI robotic systems could perform a wide range of unsafe greenhouse tasks beyond harvesting. Such applications include pesticide spraying and UV-C treatment of crops to mitigate the spread of powdery mildew. From a human health perspective, [PERSON] et al. [95] perspective on the use of robotic systems for pesticide application was validated by pesticide toxicity studies [61; 62; 96]. For example, Chlorpyrifos-methyl, a common pesticide employed in integrated pest management of the South American tomato pinworm, caused cholinesterase inhibition in humans and death in severe cases [62]. In other studies, mechanical AI systems enabled farmers to reduce their labor requirements by up to 90% [13]. However, the actual reduction rates can be contested, considering robots cannot entirely replace human labor. Empirical data supported the arguments concerning costs and operational efficiency of AI systems in farms. [PERSON] et al. [97] used Dynamic Artificial Bee-Ant Colony algorithm (DABACA) to optimize agricultural machinery plant operational cycle and route planning. The study reported better optimization, convergence accuracy (>90%), and reduced operational cycles, which translated to significant cost savings. The observations made by [PERSON] et al. [97] using DABACA were in line with [PERSON] et al. [98], whose bio-inspired algorithm-mediated task assignment of agricultural machinery, which employed ant colony algorithm-based B-patterns to improve the operational efficiency of agricultural equipment, including the fuel used and non-working distance covered during routine farm operations. However, critics argue that the positive outcomes drawn from experimental studies, such as the observations made by [PERSON] et al. [97] and [PERSON] et al. [98], did not justify the widespread adoption of AI systems in greenhouses. The concerns were premised on the following factors. First, AI systems in agriculture lacked standardized accuracy rates. The system accuracy varied depending on the local environmental conditions and operational factors. Second, the risk of error remained regardless of the algorithm's robustness [99; 100]. However, the higher risk of errors was not ubiquitous. [PERSON] et al. established that RNN (Recurrent neural network)-based decoder-encoder systems exhibited great potential in the early detection of anomalies [101]. Similarly, [PERSON] et al. noted that Elman Recurrent Neural Network (ERNN) was 98% accurate in predicting greenhouse temperature and humidity levels. The case-specific error theory provided better context concerning the benefits and limits of AI systems in smart greenhouses. Even though [99; 100] raised valid concerns about the constraints, commercial adoption should be encouraged. The positive assessment of AI systems in commercial agriculture was validated by successful commercial adoption. For example, [PERSON] et al. [102] used an ANN model to quantify crop evapotranspiration rates in Saudi Arabia. The ANN model yielded better estimates than traditional approaches to estimating evapotranspiration. Accurate determination of the plant evapotranspiration rates provided useful data for automated irrigation algorithms.