journal article·2025-02-17Open access

AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context

Javier Arévalo-Royo, Francisco-Javier Flor-Montalvo, Juan-Ignacio Latorre-Biel, Rubén Tino-Ramos, Eduardo Martínez-Cámara, Julio Blanco-Fernández

Applied Sciences· Volume 15· Issue 4

Abstract

This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Smart Agriculture and AI · Food Supply Chain Traceability · Spectroscopy and Chemometric Analyses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Smart Cities (ISC), Public University of Navarre, 31006 Pamplona, Spain

Higher School of Engineering and Technology, International University of La Rioja (UNIR), 26004 Logroño, Spain

Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, Spain

References (86)

Show source-provided references
1. Russell, S.J., and Norvig, P. (2016). Artificial Intelligence: A Modern Approach, Pearson.
2. Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer.
3. An, S., Lee, M., Park, S., Yang, H., and So, J. (2020). An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition. arXiv.
4. Deep Learning in Agriculture: A Survey10.1016/j.compag.2018.02.016 ↗
5. García-Vera, Y.E., Polochè-Arango, A., Mendivelso-Fajardo, C.A., and Gutiérrez-Bernal, F.J. (2024). Hyperspectral Image Analysis and Machine Learning Techniques for Crop Disease Detection and Identification: A Review. Sustainability, 16.10.3390/su16146064 ↗
6. Mohanty, S.P., Hughes, D.P., and Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci., 7.10.3389/fpls.2016.01419 ↗
7. Monitoring the Incidence of Xylella Fastidiosa Infection in Olive Orchards Using Ground-Based Evaluations, Airborne Imaging Spectroscopy and Sentinel-2 Time Series through 3-D Radiative Transfer Modelling10.1016/j.rse.2019.111480 ↗
8. Wheat Yield Prediction Using Machine Learning and Advanced Sensing Techniques10.1016/j.compag.2015.11.018 ↗
9. Support Vector Machines10.1002/wics.49 ↗
10. Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—A Review10.1007/s11947-016-1767-1 ↗
11. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18.10.3390/s18082674 ↗
12. Machine Learning-Driven Assessment of Heavy Metal Contamination in the Impounded Lakes of China’s South-to-North Water Diversion Project: Identifying Spatiotemporal Patterns and Ecological Risks10.1016/j.jhazmat.2024.135983 ↗
13. Suarez Baron, M.J., Gomez, A.L., and Diaz, J.E.E. (2022). Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops. Appl. Sci., 12.10.3390/app12189371 ↗
14. Hastie, T., Tibshirani, R., and Friedman, J.H. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.10.1007/978-0-387-21606-5 ↗
15. Zhu, W., Miao, J., Qing, L., and Chen, X. (2015). Deep Trans-Layer Unsupervised Networks for Representation Learning. arXiv.
16. Assessing the Accuracy of Mosaics from Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat10.1007/s11119-013-9335-4 ↗
17. A Phenological-Knowledge-Independent Method for Automatic Paddy Rice Mapping with Time Series of Polarimetric SAR Images10.1016/j.isprsjprs.2024.09.035 ↗
18. Beyond Supervision: Harnessing Self-Supervised Learning in Unseen Plant Disease Recognition10.1016/j.neucom.2024.128608 ↗
19. Moussaid, A., El Fkihi, S., and Zennayi, Y. (2021). Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms. J. Imaging, 7.10.3390/jimaging7110241 ↗
20. Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.10.1017/CBO9780511801389 ↗
21. Kernel-Based Methods for Hyperspectral Image Classification10.1109/TGRS.2005.846154 ↗
22. Support Vector Machines for Classification in Remote Sensing10.1080/01431160512331314083 ↗
23. Automatic Grape Bunch Detection in Vineyards with an SVM Classifier10.1016/j.jal.2015.06.001 ↗
24. Machine Learning-Driven Modeling for Soil Organic Carbon Estimation from Multispectral Drone Imaging: A Case Study in Corvera, Murcia (Spain)10.1007/s40808-024-01963-y ↗
25. Islam, S., Reza, M.N., Lee, K.-H., Ahmed, S., Cho, Y.J., Noh, D.H., and Chung, S.-O. (2024). Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory. Agronomy, 14.10.3390/agronomy14092043 ↗
26. Machine Learning-Based Smart Irrigation Controller for Runoff Minimization in Turfgrass Irrigation10.1016/j.atech.2024.100569 ↗
27. Early Prediction of Maize Resistance to Nicosulfuron Using Hyperspectral Imaging and Deep Learning: Method and Mechanism10.1016/j.compag.2024.109511 ↗
28. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley.
29. Application of Genetic Algorithm in Production Scheduling: A Case Study on the Food Processing Business
30. Real-Time Energy Optimization of Irrigation Scheduling by Parallel Multi-Objective Genetic Algorithms10.1016/j.agwat.2019.105857 ↗
31. An Approach for Crop Prediction in Agriculture: Integrating Genetic Algorithms and Machine Learning10.1109/ACCESS.2024.3478739 ↗
32. Decision Support System Based on Genetic Algorithms to Optimize the Daily Management of Water Abstraction from Multiple Groundwater Supply Sources10.1007/s11269-020-02687-1 ↗
33. Fundamental Concepts of Convolutional Neural Network
34. Lightweight Deep Learning Model for Underwater Waste Segmentation Based on Sonar Images10.1016/j.wasman.2024.09.008 ↗
35. Thakur, P.S., Khanna, P., Sheorey, T., and Ojha, A. (2022). Explainable Vision Transformer Enabled Convolutional Neural Network for Plant Disease Identification: PlantXViT. arXiv.10.1016/j.ecoinf.2023.102245 ↗
36. Short-Term Spatio-Temporal Forecasting of Air Temperatures Using Deep Graph Convolutional Neural Networks10.1007/s00477-022-02358-0 ↗
37. Deep Leaning Approach with Colorimetric Spaces and Vegetation Indices for Vine Diseases Detection in UAV Images10.1016/j.compag.2018.10.006 ↗
38. Canopy Height Uniformity: A New 3D Phenotypic Indicator Linking Individual Plant to Canopy10.1016/j.compag.2024.109491 ↗
39. K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA10.33003/fjs-2020-0402-363 ↗
40. Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture. Sensors, 20.10.3390/s20092530 ↗
41. Random Forest in Remote Sensing: A Review of Applications and Future Directions10.1016/j.isprsjprs.2016.01.011 ↗
42. Explainable Olive Grove and Grapevine Pest Forecasting through Machine Learning-Based Classification and Regression10.1016/j.rineng.2024.103058 ↗
43. The Impact of Managed Aquifer Recharge on the Fate and Transport of Pesticides in Agricultural Soils10.1016/j.watres.2024.122442 ↗
44. Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning10.1109/ACCESS.2023.3321861 ↗
45. Long-Term Annual Estimation of Forest above Ground Biomass, Canopy Cover, and Height from Airborne and Spaceborne Sensors Synergies in the Iberian Peninsula10.1016/j.envres.2024.119432 ↗
46. Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction, MIT Press.10.1109/TNN.1998.712192 ↗
47. Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications10.1109/ACCESS.2020.2992480 ↗
48. Reinforcement Learning-Based Assimilation of the WOFOST Crop Model10.1016/j.atech.2024.100604 ↗
49. Deep Reinforcement Learning-Based Irrigation Scheduling10.13031/trans.13633 ↗
50. Reinforcement Learning for Crop Management Support: Review, Prospects and Challenges10.1016/j.compag.2022.107182 ↗
51. Nguyen, T.T., and Brownstein, K.J. (2024). Utilization of a Natural Language Processing-Based Approach to Determine the Composition of Artifact Residues. BMC Bioinform., 25.10.1186/s12859-024-05888-2 ↗
52. Hernández-Romero, I.M., Niño-Caballero, J.C., González, L.T., Pérez-Rodríguez, M., Flores-Tlacuahuac, A., and Montesinos-Castellanos, A. (2024). Waste Management Optimization with NLP Modeling and Waste-to-Energy in a Circular Economy. Sci. Rep., 14.10.1038/s41598-024-69321-7 ↗
53. Natural Language Processing of Social Network Data for the Evaluation of Agricultural and Rural Policies10.1016/j.jrurstud.2024.103341 ↗
54. Dried-Meats Descriptive Cards in Spanish and English: A Corpus Based Study of the Rhetoric Structure
55. Ross, T.J. (2016). Fuzzy Logic with Engineering Applications, Wiley.
56. Development of a Fuzzy Logic Controller Applied to an Agricultural Greenhouse Experimentally Validated10.1016/j.applthermaleng.2018.06.014 ↗
57. Toward Sustainable Greenhouses: Self-Tuning Adaptive Temperature Control and Its Renewable Energy Potential10.1016/j.egyr.2024.07.043 ↗
58. Improving Weather Dependent Zone Specific Irrigation Control Scheme in IoT and Big Data Enabled Self Driven Precision Agriculture Mechanism10.1080/17517575.2020.1713406 ↗
59. Load Forecasting Using Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Approaches: Application to South-Western Morocco
60. Kim, T.H., Shahroz, M., Alabdullah, B., Innab, N., Baili, J., Umer, M., Majeed, F., and Ashraf, I. (2024). ANFIS Fuzzy Convolutional Neural Network Model for Leaf Disease Detection. Front. Plant Sci., 15.10.3389/fpls.2024.1465960 ↗
61. Kennedy, J., Eberhart, R.C., and Shi, Y. (2001). Swarm Intelligence, Elsevier Science.
62. Optimization of Reservoir Operation by Sine Cosine Algorithm: A Case of Study in Algeria
63. In-Field Performance Evaluation of Robotic Arm Developed for Harvesting Cotton Bolls10.1016/j.compag.2024.109517 ↗
64. Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture10.1109/JSEN.2019.2940186 ↗
65. Advanced Evapotranspiration Forecasting in Central Italy: Stacked MLP-RF Algorithm and Correlated Nystrom Views with Feature Selection Strategies10.1016/j.compag.2024.108887 ↗
66. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, Wiley.
67. Dhal, S.B., Kalafatis, S., Braga-Neto, U., Gadepally, K.C., Landivar-Scott, J.L., Zhao, L., Nowka, K., Landivar, J., Pal, P., and Bhandari, M. (2024). Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops. Remote Sens., 16.10.3390/rs16111906 ↗
68. Time Series Modelling of Drought in a Semi-Arid Region of South Gujarat, India10.1007/s12040-024-02357-5 ↗
69. A Systematic Study Involving Patent Analysis and Theoretical Modeling of Eco-Friendly Technologies for Electric Vehicles and Power Batteries to Ease Carbon Emission from the Transportation Industry10.1016/j.enconman.2024.118996 ↗
70. Advancements in Weather Forecasting for Precision Agriculture: From Statistical Modeling to Transformer-Based Architectures10.1007/s00477-024-02778-0 ↗
71. Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, Morgan Kaufmann.
72. Imran, S., Sarker, P., Mahamud, M.A., Paul, N.C., Chakrobortty, J., Harine, I.J., Rahman, M.A., and Rahimi, M. (2024). Copper Mitigates Salinity Stress by Regulating Water Status, Photosynthetic Pigments and Ion Homeostasis and Increases the Yield of Eggplant (Solanum Melongena). BMC Plant Biol., 24.10.1186/s12870-024-05625-0 ↗
73. A Portable Europium Complex-Loaded Fluorescent Test Paper Combined with Smartphone Analysis for the on-Site and Visual Detection of Mancozeb in Food Samples10.1016/j.foodchem.2024.140311 ↗
74. Sustainable Agricultural Technologies of the Future: Determination of Adoption Readiness for Different Consumer Groups10.1016/j.techfore.2024.123697 ↗
75. Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data10.1016/j.rse.2024.114110 ↗
76. Improving Crop Yield Prediction Accuracy by Embedding Phenological Heterogeneity into Model Parameter Sets10.1016/j.agsy.2023.103666 ↗
77. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
78. Optimizing Starch Content Prediction in Kudzu: Integrating Hyperspectral Imaging and Deep Learning with WGAN-GP10.1016/j.foodcont.2024.110762 ↗
79. Shape and Style GAN-Based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming10.1016/j.cropro.2024.106848 ↗
80. LeafNST: An Improved Data Augmentation Method for Classification of Plant Disease Using Object-Based Neural Style Transfer10.1007/s44163-024-00150-3 ↗
81. Evaluation of Synthetic Data Generation for Intelligent Climate Control in Greenhouses10.1007/s10489-023-04783-2 ↗
82. An Intelligent Edge-IoT Platform for Monitoring Livestock and Crops in a Dairy Farming Scenario10.1016/j.adhoc.2019.102047 ↗
83. More than One Million Barriers Fragment Europe’s Rivers10.1038/s41586-020-3005-2 ↗
84. Heintz, F., Belbachir, N., and Curry, E. (2024, December 12). Strategic Research, Innovation, and Deployment Agenda 2025–2027. Available online: https://adr-association.eu/news/adra-releases-strategic-research-innovation-and-deployment-agenda.
85. de Castro, A.I., Peña, J.M., Torres-Sánchez, J., Jiménez-Brenes, F.M., Valencia-Gredilla, F., Recasens, J., and López-Granados, F. (2019). Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture. Remote Sens., 12.10.3390/rs12010056 ↗
86. Detección de Malas Hierbas En Girasol En Fase Temprana Mediante Imágenes Tomadas Con Un Vehículo Aéreo No Tripulado (UAV)