# 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
Beyond the estimation of the crop evapotranspiration rates, the ANN and WDNN algorithms were equally proven useful in predicting the photosynthetic rates of plants, among other physiological indices that predict plant growth rates and yield [103]. In other cases, hybrid algorithms were proven reliable in weed prediction/optimization [104; 105], and the determination of the soil cation exchange capacity [106]. The positive assessment of ANN technologies reinforces earlier observations by [PERSON] et al. [101] on the fusion of deep learning and artificial intelligence (RNN encoders and LSTM (Long Short-Term Memory)) to facilitate learning patterns in time series and modeling data sequences using monitored parameters to predict crop growth indicators in greenhouses. The case for RNN advanced by [PERSON] et al. [101] negated critical shortcomings identified by [PERSON] et al. [107]; NN systems were inferior to LSTM owing to the long time series and vanishing gradient problem, which made it difficult to train the model on long-term dependencies. The latter findings validate the emerging transition from LSTM to RNN. [PERSON] et al. [102] and [PERSON] et al. 's [103] bias toward new algorithms for greenhouses was validated by the following considerations. First, modern greenhouses are complex; there are dynamic relationships between different greenhouse parameters, and the data mining ability of traditional models is insufficient to facilitate the extraction of all useful information. Second, the traditional models' low precision and convergence speeds made them incompatible with modern intelligent greenhouses. On the downside, the research of [PERSON] et al. [102] and [PERSON] et al. [103] negated the fact that the transition from traditional to modern algorithms does not automatically translate to higher prediction accuracy and sensitivity. The hybrid algorithms must be combined with the appropriate classifiers to yield better accuracy and specificity [105]. For example, [PERSON] and [PERSON] achieved a 92% accuracy with the CNN (Convolutional Neural Network)-Support Vector Machine (SVM) algorithm for weed recognition [104]. However, SVM coupled with the ANN algorithm proved inferior compared to the emerging class of deep learning models. CNN was paired with SVM because the former exhibited satisfactory results in computer pattern recognition, but unsatisfactory results in artificial differentiation of weeds. The SVM model helped to address this challenge [104]. In contrast to [PERSON] and [PERSON] [104], [PERSON] et al. [106] proposed a three-model algorithm integrating particle swarm, integrated invasive weed optimization, and support vector for accurate weed recognition. Drawing from the latter study, the development of algorithms for evaluating greenhouse parameters should be customized to match local conditions and requirements. The findings of [PERSON] et al. [106] were corroborated by [PERSON] et al., who noted that the integrated particle swarm algorithm was ideal for selecting discriminant features [108]. On the downside, widespread adoption is constrained by a paucity of temporal data for precise predictions and training of AI and ML systems. [PERSON] et al. argued that the lack of suitable historical data would be a critical impediment to the adoption of AI systems in agriculture. The latter findings contradict the positive narratives advanced by [PERSON] et al. [106] and [PERSON] et al. [108] concerning the immense potential of CNN and ANN algorithms in intelligent farming. The increased reliance on bio-inspired algorithms in crop irrigation is justified, given traditional irrigation methods resulted in a 60% waste of water resources [38]. The suitability of different algorithms in predicting the evapotranspiration rates is depicted in Table 1. The data show that ELM-GA, ELM-CSA, ELM-ACO, and ELM PFA were ideal models for determining the evapotranspiration rates, since the \(R^{2}\) (coefficient of determination) values were close to unity.