# 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
These developments underscore the transformative potential of genetic algorithms in enhancing operational efficiencies and promoting sustainable practices within the agrifood sector. By enabling optimized scheduling and resource management, GAs contribute significantly to reducing costs, minimizing waste, and improving overall productivity, thereby supporting the industry's sustainability objectives. Convolutional Neural Networks (CNNs): A Specialized Deep Learning Architecture for Grid-Structured Data  
Convolutional neural networks (CNNs) represent an advanced category of neural networks meticulously designed to process data with grid-like structures, such as images, with exceptional efficiency. These networks utilize convolutional layers to autonomously and hierarchically extract features from input data, thereby rendering them particularly efficient at tasks involving pattern recognition and image classification [33]. [PERSON] and [PERSON] [34] present a lightweight deep learning model using CNNs for image segmentation tasks, demonstrating its potential in agricultural applications such as precision monitoring of crop health and waste management. Although developed primarily for underwater waste segmentation, the model's architecture is optimized for high efficiency in complex environments, which translates well to agricultural settings where resource constraints are common. This CNN approach can facilitate the detection of crop residues and contaminants, offering a scalable solution for enhancing the cleanliness and sustainability of farmland. Their work underscores the adaptability of CNNs across diverse agricultural challenges, from monitoring soil quality to supporting eco-friendly waste management practices. In agricultural applications, convolutional neural networks (CNNs) have been effectively utilized for plant disease identification ref. [35] using a hybrid model combining CNNs with Vision Transformers. The proposed PlantXViT model comprises a lightweight structure with only 0.8 million trainable parameters, making it suitable for Internet of Things (IoT)-based smart agriculture services. The model was trained and evaluated on five publicly available datasets, achieving an average accuracy exceeding 93.55% on the Apple dataset, 92.59% on the Maize dataset, and 98.33% on the Rice dataset, even under challenging background conditions. These results underscore the efficacy of integrating CNN architectures with Vision Transformers and utilizing high-quality data sources in supervised learning applications within agriculture. Another illustrative application of convolutional neural networks (CNNs) in an environmental context relevant to agriculture is short-term spatio-temporal forecasting of air temperatures, as investigated by [PERSON] et al. [36]. Their study used deep graph CNNs to predict meteorological variables critical for agricultural planning. The proposed approach is validated using real, open weather data from 37 meteorological stations in Spain, ensuring its applicability to regional agricultural contexts. Although focused on environmental forecasting, the method is highly relevant to precision agriculture, where accurate climatic predictions enhance irrigation and crop management strategies. This integration underscores the versatility of CNN-based models in supporting data-driven agricultural decision-making. Given the paramount significance of the viticulture sector in various regions of Spain, the ability to efficiently process large datasets through CNNs is particularly noteworthy. These networks facilitate real-time monitoring and the seamless integration of vegetation indices with other remote sensing data, thereby improving the precision of plant health assessments and contributing to more informed agricultural decision-making in large-scale viticulture operations. For example, [PERSON] et al. [37] used CNNs for the early detection of diseases in vineyards using UAV imagery, thereby enabling timely and targeted management interventions to mitigate vine diseases.