# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry
### Random Forest: A Supervised Ensemble Learning Algorithm Using Decision Trees
Similarly, [PERSON] et al. [58] used fuzzy logic to enhance irrigation practices in precision agriculture. By utilizing weather-dependent, zone-specific irrigation strategies, their approach significantly improves water use efficiency. Furthermore, [PERSON] et al. [59] used fuzzy logic to enhance irrigation practices in precision agriculture. By utilizing weather-dependent, zone-specific irrigation strategies, their approach significantly improves water use efficiency. In agricultural applications, fuzzy logic has been effectively utilized for plant disease detection [60] with a hybrid model combining Adaptive Neuro-Fuzzy Inference System (ANFIS) with convolutional neural networks (CNNs). The model was trained on a dataset comprising images of diseased and healthy plant leaves. The architecture integrates fuzzy logic principles to handle uncertainty and imprecision in image data, enhancing the model's robustness. The proposed model achieved an accuracy score of 84.78% without using Local Binary Pattern (LBP) features, while its precision, recall, and F1 scores were 89.59%, 90.45%, and 89.53%, respectively. These results underscore the efficacy of incorporating fuzzy logic into CNN architectures for plant disease detection, highlighting the importance of selecting appropriate model architectures and utilizing high-quality data sources in agricultural applications. Particle Swarm Optimization (PSO): An Evolutionary Optimization Algorithm Inspired by Collective Behaviors in Nature  
PSO is a nature-inspired algorithm designed to solve complex optimization problems by emulating the social behavior of organisms, such as bird flocking or fish schooling. PSO operates by utilizing a group of particles that explore the search space collaboratively, with each particle adjusting its position based on both its individual experiences and the shared knowledge of the swarm. Through this iterative process, the algorithm converges efficiently toward optimal solutions. In the agrifood industry, PSO proves particularly useful in addressing logistical and resource management challenges. For instance, it can be used to optimize transportation routes, reducing fuel consumption and delivery times, or to improve the distribution of water resources for irrigation. Moreover, PSO has been applied to enhance energy efficiency in agricultural facilities by optimizing operational parameters, thereby contributing to a significant reduction in operational costs. These applications of PSO contribute to more sustainable and cost-effective practices across the sector, as discussed in \"Swarm Intelligent\" [61]. Recent advancements in particle swarm optimization (PSO) algorithms showcase their versatility across various sectors, from energy management to precision agriculture. For instance, [PERSON] et al. [62] applied a sine-cosine PSO variant to optimize reservoir operations, achieving efficient water distribution while accounting for environmental constraints. This study exemplifies how PSO can adapt complex systems to real-world limitations in resource management. In the field of agricultural automation, [PERSON] et al. [63] developed a PSO-based robotic arm for precision harvesting, particularly in cotton farming. Their model enhances in-field performance by optimizing movement paths to reduce resource use and operational time. Another notable application by [PERSON] et al. [64] utilizes PSO in wireless sensor networks to improve path-lossmodels, which strengthens connectivity in remote agricultural settings. [PERSON] et al. [65] explored the application of PSO algorithms for advanced evapotranspiration forecasting in Mediterranean agricultural settings. Their research, conducted in central Italy, combined PSO with feature selection methods to improve the accuracy of water demand predictions.