# 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
### Genetic Algorithms: An Optimization Algorithm Inspired by Evolutionary Biology  
Genetic algorithms (GAs) represent a sophisticated class of optimization techniques inspired by the principles of natural selection and genetics. These algorithms simulate evolutionary processes to resolve complex problems by generating a population of potential solutions and iteratively applying selection, crossover, and mutation operators to evolve solutions with enhanced fitness across successive generations ([PERSON] et al., 2018). Within the agrifood industry, GAs are optimally used to determine the most efficient production schedules in food manufacturing facilities, such as optimizing the sequence of processing tasks in dairy plants. By considering factors such as processing times, resource constraints, and delivery deadlines, GAs can generate optimized schedules that bolster operational efficiency, reduce production costs, and ensure the timely delivery of fresh products. This application not only increases productivity, but also contributes to waste reduction and increased customer satisfaction within the agrifood supply chain. For instance, [PERSON] et al. [29] applied genetic algorithms to optimize the scheduling of batch processing in food production, resulting in significant cost savings. In the Spanish context, GAs have been used to optimize the energy costs associated with irrigation networks, encompassing both drip and sprinkler systems. The escalating energy expenses linked to these systems, primarily due to pressure demands, have demanded innovative solutions. Spanish researchers have demonstrated that effective management of irrigation schedules through GAs can substantially reduce energy expenditures by approximately 6-7%. Traditionally, irrigation optimization required adherence to stringent minimum pressure requirements, which posed computational challenges. However, by reorganizing irrigation schedules and integrating daily volume requirements at each hydrant, this approach not only minimizes energy consumption but also ensures adequate pressure at critical hydrants [30]. The Spanish implementation achieves remarkable efficiency through a multi-objective approach, coupled with the parallel evaluation of objective functions, thereby accelerating the optimization process by an order of magnitude. This advancement facilitates daily irrigation optimization, thereby fostering greater sustainability in agricultural practices across Spain. In agricultural applications, genetic algorithms (GAs) have been effectively utilized to optimize predictive models for crop yield forecasting. A notable recent study by [PERSON] et al. [31] presents a hybrid model combining GAs with a random forest (RF) classifier. The model was trained on a dataset encompassing various features such as soil composition, weather conditions, and historical crop yields. The GA component optimized the hyperparameters of the RF classifier, enhancing its predictive performance. This integrated approach achieved an impressive accuracy rate of 99.3% in predicting the outcomes for 22 different crops. The study underscores the efficacy of combining genetic algorithms with machine learning techniques to enhance predictive accuracy in agricultural applications. Another notable example of genetic algorithms (GAs) applied in the Spanish agrifood sector are optimizing irrigation networks, as explored by [PERSON] et al. [32]. Their study, conducted at the University of Castilla-La Mancha in Spain, proposed a decision support system based on GAs for managing irrigation schedules in agricultural fields. The approach integrated variable-speed pumping technologies to reduce water and energy consumption while ensuring optimal irrigation coverage. By dynamically adjusting water delivery schedules, the system achieved significant improvements in resource efficiency, demonstrating how GAs can address pressing sustainability challenges in water-scarce agricultural regions.