# 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  
A seminal study by [PERSON] and [PERSON] [41] demonstrated the superiority of random forest in land cover classification using Sentinel-2 data. Building upon their findings, subsequent research has corroborated that RF classifiers exhibit high effectiveness in remote sensing applications, particularly excelling with high-dimensional datasets such as hyperspectral imagery. The inherent resistance of random forest to overfitting, even when dealing with complex training samples, ensures a reliable classification accuracy. Moreover, the internal variable importance measures (e.g., mean decrease in accuracy) facilitate refined feature selection, thereby enhancing model efficiency and precision across various remote sensing data types, including multispectral, radar, and LiDAR sources. Recent research further highlights the versatility of RF models in diverse agricultural contexts. For instance, [PERSON] et al. [42] present a machine learning-based approach utilizing RF models for forecasting pest infestations in olive groves and vineyards. By providing insights into pest dynamics, the model aids farmers in implementing timely interventions, thus promoting sustainable pest control practices. This research underscores the value of random forest in the agricultural sector, where predictive accuracy and model transparency are key for effective field management. [PERSON] et al. [43] investigate the effects of managed aquifer recharge (MAR) on the behavior of pesticides within agricultural soils, employing RF algorithms to model pesticide transport and retention, understanding complex soil-water interactions, and providing crucial insights for sustainable water resource management and pollution mitigation in agricultural landscapes. Random forest models have been effectively utilized too for crop yield prediction [44], implementing a random forest model to forecast crop yields. The dataset encompassed variables such as rainfall, crop type, meteorological conditions, area, production, and yield. The random forest model achieved a maximum accuracy of 98.96%, a mean absolute error of 1.97, a root mean square error of 2.45, and a standard deviation of 1.23. These results underscore the efficacy of random forest models in agricultural yield prediction, highlighting the importance of selecting appropriate model architectures and utilizing high-quality data sources in supervised learning applications. Another notable example of random forest application in the Spanish agricultural context is the estimation of forest above-ground biomass, as investigated by [PERSON] et al. [45]. Their study used random forest models to estimate biomass in Mediterranean forests ecosystems using remote sensing data from multispectral and LiDAR sensors. The researchers developed and tested a comprehensive framework to map forest above-ground biomass, canopy cover, and forest height across two ecologically significant regions in peninsular Spain: the Mediterranean biome in the Madrid region and the temperate biome in the Basque Country. This approach demonstrated high accuracy in biomass prediction, providing essential insights into forest management and environmental monitoring. The study underscores how random forest can support sustainable land management practices through data-driven decision-making and precise environmental assessment. Reinforcement Learning: A Paradigm of Autonomous Learning Focused on Decision-Making Through Interaction with an Environment  
Reinforcement Learning (RL) constitutes a distinct paradigm within machine learning, emphasizing the decision-making processes of agents operating within dynamic environments to maximize cumulative rewards. [PERSON] and [PERSON] provide a comprehensive introduction to this methodology in \"Reinforcement Learning: An Introduction\", wherein they elaborate on its foundational principles [46] and delve into practical applications and solution methods in subsequent chapters.