# 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
### Support Vector Machines (SVMs): A Supervised Learning Algorithm  
Support vector machines (SVMs) constitute a prominent class of supervised learning models extensively used for both classification and regression tasks. SVMs function by identifying the optimal hyperplane that segregates data points into distinct categories. This methodological approach renders SVMs exceptionally efficient at managing complex datasets, particularly when integrated with kernel methods, which empower the model to capture intricate non-linear relationships [20]. Owing to their robustness in handling noisy datasets and their adaptability through kernel techniques, SVMs have proven highly effective in the detection and identification of pests and plant diseases. By scrutinizing spectral data or imagery obtained from sensors and drones, SVMs can discern between healthy and afflicted crops with remarkable precision. This enhanced classification capability facilitates more accurate and targeted interventions in crop health management. A pioneering and enduring example demonstrating the resilience of SVMs in agricultural applications is presented by [PERSON] and [PERSON] [21]. Nearly two decades ago, their study established a foundational benchmark by showcasing the efficacy of SVMs in classifying hyperspectral images of agricultural regions. By adeptly handling the high-dimensionaland noisy data inherent in such imaging techniques, the authors illustrated that SVMs achieved superior classification accuracy compared to traditional classifiers, successfully differentiating between various crop types and soil conditions. Since then, SVMs have been extensively used for similar purposes in numerous studies. Thereby, contributing to more efficient resource allocation and enhanced overall agricultural productivity. In contemporary applications, [PERSON] and [PERSON] [PERSON] and [PERSON] [PERSON] and [PERSON] (2015) leveraged SVMs for land cover classification utilizing remotely sensed data, a critical task for monitoring agricultural landscapes and orchestrating resource distribution. Additionally, SVMs have been applied to classify vineyard conditions based on leaf spectral signatures, thereby supporting precision viticulture [PERSON] and [PERSON] (2015). Building on this, [PERSON] et al. conducted a case study in Corvera, Murcia, employing SVM-based models for spatial prediction of soil organic carbon in Mediterranean agricultural landscapes. Their approach effectively captured spatial variability, outperforming conventional interpolation techniques and enabling more accurate soil mapping. This highlights the adaptability of SVMs in precision agriculture, particularly in regions such as Spain and southern Europe, where soil heterogeneity plays a critical role in determining crop yields [PERSON] and [PERSON] (2015). In the field of agriculture, support vector machines (SVMs) have been effectively applied to classify stress conditions in pepper seedlings [PERSON] and [PERSON] (2015). Researchers cultivated two-week-old pepper seedlings under varying environmental conditions, including different temperatures (20, 25, and 30\({}^{\circ}\)C), light intensities (50, 250, and 450 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), and photoperiods (8/16, 10/14, and 16/8 h). Daily images of the seedling canopies were captured using a low-cost RGB camera over a two-week period. From these images, 18 color features, 9 texture features using the gray-level co-occurrence matrix (GLCM), and 1 morphological feature were extracted. A sequential feature selection (SFS) method was applied to identify the optimal features for the classification model. The SVM model, evaluated with the receiver operating characteristic (ROC) curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. These implementations underscore the versatility and effectiveness of SVMs in diverse agricultural contexts.