# 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
#### 2.4.2. Commercial Development of Robotic Systems for Smart Agriculture (Inhouse and Outdoors)  
In 2022, John Deere Company (one of the world's leading farm machinery manufacturers) released fully autonomous tractors for precision agriculture featuring the next  
Figure 6: (**A**,**C**) show the average population of _Nesidiocoris tenuis_ and _Tuta absoluta_ per leaf inside a commercial greenhouse in 2016, and (**B**,**D**) show the average population of the same pests in 2017 after the pesticide was applied twice in 2016 [61]. generation optical and electro-optical cameras. The cameras helped to detect obstacles and calculate distances in real time [130]. The utility of advanced computer vision systems in robotics was affirmed by [PERSON] et al. [131]. However, in the latter case, performance was augmented by the integration of image processing algorithms. The robotic system accurately distinguished tomatoes, leaves, and wood support structures (see Figure 7). Despite the milestones recorded in recent years, a major concern was the lack of consensus on the most effective method for robotic automation and mobility. Drawing from [PERSON] et al. [131] and [PERSON] [130], LIDAR and optical cameras yielded superior outcomes. However, not all scholars support the case for LIDAR and optical cameras. For example, [PERSON] and [PERSON] [132] argued that satellite-differential GPS (geographic positioning systems) (S-DGPS) connected to a John Deere tractor (ground station) proved to be reliable and accurate in facilitating autonomous mobility. The John Deere-S-DGPS system had an accuracy of up to 2 cm from the target. In other cases, the robotic systems achieved an accuracy of less than a centimeter [132]. In contrast to other AI and ML systems, which recorded variable results under different environments and operating conditions, the G-DGPS had consistent accuracy in Israel, Georgia, and the US (California) [132]. The mixed observations of [PERSON] and [PERSON] [132] and [PERSON] et al. [131] about the utility of GPS systems in agricultural robots show that contextual factors and technological improvements were important moderating factors. Additionally, one cannot disregard the legitimate concerns raised about robotic system accuracy. The issues raised by [PERSON] [8] concerning the inconsistent accuracy of AI systems in smart greenhouses were in agreement with [PERSON], [PERSON], and [PERSON]'s [133] research, which reported a low prediction accuracy for the Adaptive Neuro-Fuzzy Inference System (ANFIS) coupled with bio-inspired optimization algorithms. However, [PERSON], [PERSON], and [PERSON] [133] and [PERSON] [8] contradict [PERSON] et al. [134], who reported a >93.6% prediction accuracy using ANFIS and IoT. The higher accuracy reported by [PERSON] et al. [134] reinforces the perspectives of [PERSON] et al. on achieving an optimal balance between scalability, computational speed, and accuracy of the AI systems. The inconsistencies in the accuracy of ANFIS-AI systems with bio-inspired algorithms can be partly linked to the distinct operational parameters and optimum operational conditions. The later claims aligned with the research of [PERSON] et al. [135] on optimal target identification using multiple unmanned UAVs guided by bio-inspired algorithms to identify crop diseases. On a positive note, the lower prediction accuracy reported by [PERSON] et al. [133] and [PERSON] [8] could be resolved using digital models based on model predictive control. [PERSON] et al. confirmed that the MPC (model predictive control) digital models improved accuracy without compromising the computational speed [136]. On the downside, the positive assessment of MPCs by [PERSON] et al. [136] negated the fact  
Figure 7: Object classification, localization, detection, and instance segmentation using image processing algorithms in agricultural robots [131]. that the classical controller systems had critical shortcomings and accurate operations depended on the mathematical models' accuracy [10].