# 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
### Unsupervised Learning: A Subset of the Machine Learning Family  
Unsupervised learning algorithms, engineered to uncover intrinsic patterns within unlabeled datasets, are meticulously examined in \"The Elements of Statistical Learning\" [14] by [PERSON] et al. These algorithms are particularly efficient in environments where explicit labels or predefined categories are absent, thereby proving invaluable for identifying underlying structures within intricate data landscapes. A notable example is the application of autoencoders for anomaly detection in image data. Autoencoders are neural networks designed to learn efficient coding of input data, typically consisting of an encoder that maps the input into a latent space and a decoder that reconstructs the original input from this representation. For instance, in a study on unsupervised feature learning, a deep trans-layer network was employed, achieving 99.45% accuracy on the MNIST dataset, which comprises 60,000 training images and 10,000 testing images of 28 \(\times\) 28 pixel grayscale handwritten digits [15]. In the agrifood industry, unsupervised learning methodologies are adeptly applied to segment cultivation zones based on attributes such as soil properties, temperature variations, and humidity levels. This data-driven segmentation facilitates more precise agricultural management by enabling targeted interventions tailored to each specific zone. For instance, clustering techniques such as k-means and hierarchical clustering are effectively used to partition fields into smaller, more manageable sections, thereby optimizing the allocation of resources such as water and fertilizers. Through these applications, unsupervised learning algorithms contribute to the promotion of sustainable practices, enhancement of productivity, reduction of waste, and overall improvement of operational efficiency across diverse farmland areas. Their ability to process extensive datasets and adapt to evolving conditions further underscore their practicality and reliability in the context of precision agriculture. Within the Spanish agrifood sector, unsupervised learning has been instrumental in optimizing wheat crops. [PERSON] et al. [16] used clustering algorithms to segment wheat groves based on canopy characteristics derived from aerial imagery, thereby enabling differentiated harvesting strategies. Also applicable to the Spanish agrilocal sector is the research by [PERSON] et al. [17] which explored clustering techniques in multitemporal crop phenology monitoring, developing a model for detecting growth stages in rice fields through satellite data--an approach that is both adaptable to other crops and scalable for smaller plots in data-limited environments. Additional studies exemplify the extensive applications of clustering and unsupervised learning in agriculture. For example, a recent hybrid model proposed by [PERSON] et al. [18] integrates self-supervised learning with forecasting techniques to enhance resource management in complex environments, with applications to energy-efficient processes in agricultural facilities. This integration underscores a synergy between advanced computational approaches and sustainable practices, emphasizing the role of hybrid learning in optimizing resource allocation and minimizing environmental impacts within the agrifood sector. An illustrative implementation of unsupervised learning relevant to the Spanish agrifood context is the segmentation and classification of tree crowns in overlapping orchards, as explored by [PERSON] et al. [19] Although conducted in Morocco, the study's focus on citrus orchards within a Mediterranean-like climate mirrors the agricultural conditions of Spanish regions such as Valencia or Murcia. This reference was selected from the dataset obtained through a targeted SCOPUS search, reflecting the closest geographic and methodological relevance identified within the available academic literature.