Machine learning in ensuring food security: domestic and international practices
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Abstract
Food security remains one of the most critical global challenges, especially in the context of climate change, geopolitical instability, and rising economic inequality. In recent years, machine learning (ML) has emerged as a powerful tool to support data-driven decision-making in agriculture, food systems, and public health. This paper presents a literature review of domestic and international practices in applying machine learning methods to food security, structured around the four key pillars: availability, access, utilization, and stability. The review is based on the analysis of over 35 peer-reviewed publications from 2015 to 2025, retrieved from major databases including Scopus, Web of Science, IEEE Xplore, and AGRIS. For each application area, we examine the most commonly used ML algorithms, data sources, and validation approaches. The results show that tree-based models, deep learning architectures, and hybrid statistical-ML techniques are widely used for tasks such as crop yield forecasting, food price prediction, malnutrition detection, and early warning systems. Despite promising progress, key challenges persist. These include limited data availability in low-resource regions, lack of model interpretability, weak generalizability across contexts, and underrepresentation of Central Asia in global research. The review concludes with practical recommendations for enhancing the use of machine learning in food security monitoring, especially in the context of Kazakhstan and neighboring countries. This study contributes to the growing body of research at the intersection of artificial intelligence and sustainable development and highlights emerging opportunities for context-aware, ethical, and transparent ML applications in food systems.
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References
FAO, Report of the World Food Summit (1996).
C. M. Galanakis, M. Ι. Daskalakis, I. Galanakis, A. Gallo,
E. A. E. Marino, A. Chalkidou, & E. Agrafioti, A systematic framework for understanding food security drivers and their interactions. Discover Food, 5 (2025) 178. https://doi.org/10.1007/s44187-025-00480-w. DOI: https://doi.org/10.1007/s44187-025-00480-w
N. AL-Rousan, H. AL-Najjar, & D. AL-Najjar, The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security. Heliyon, 10 (2024) e29279. https://doi.org/10.1016/j.heliyon.2024.e29279. DOI: https://doi.org/10.1016/j.heliyon.2024.e29279
C. Collins, D. Dennehy, K. Conboy, & P. Mikalef, Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60 (2021) 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383. DOI: https://doi.org/10.1016/j.ijinfomgt.2021.102383
M. Machefer, A.-C. Thomas, M. Meroni, J. M. Veiga Lopez Pena,
M. Ronco, C. Corbane, & F. Rembold, Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity. Global Food Security, 45 (2025) 100859. https://doi.org/10.1016/j.gfs.2025.100859. DOI: https://doi.org/10.1016/j.gfs.2025.100859
A. A. Mana, A. Allouhi, A. Hamrani, S. Rehman, I. el Jamaoui, &
K. Jayachandran, Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices. Smart Agricultural Technology, 7 (2024) 100416. https://doi.org/10.1016/j.atech.2024.100416. DOI: https://doi.org/10.1016/j.atech.2024.100416
K. Singh, M. Yadav, D. Barak, S. Bansal, & F. Moreira, Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability, 17 (2025) 4711. https://doi.org/10.3390/su17104711. DOI: https://doi.org/10.3390/su17104711
M. Ashfaq, I. Khan, D. Shah, S. Ali, & M. Tahir, Predicting wheat yield using deep learning and multi-source environmental data. Scientific Reports, 15 (2025) 26446. https://doi.org/10.1038/s41598-025-11780-7. DOI: https://doi.org/10.1038/s41598-025-11780-7
M. von Bloh, R. de S. Nóia Júnior, X. Wangerpohl, A. O. Saltık,
V. Haller, L. Kaiser, & S. Asseng, Machine learning for soybean yield forecasting in Brazil. Agricultural and Forest Meteorology, 341 (2023) 109670. https://doi.org/10.1016/j.agrformet.2023.109670. DOI: https://doi.org/10.1016/j.agrformet.2023.109670
H. Fu, J. Lu, J. Li, W. Zou, X. Tang, X. Ning, & Y. Sun, Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models. Agronomy, 15 (2025) 205. https://doi.org/10.3390/agronomy15010205. DOI: https://doi.org/10.3390/agronomy15010205
M. K. Gumma, P. S. Thenkabail, P. G. Teluguntla, A. Oliphant,
J. Xiong, C. Giri, V. Pyla, S. Dixit, & A. M. Whitbread, Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. GIScience & Remote Sensing, 57 (2020) 302–322. https://doi.org/10.1080/15481603.2019.1690780. DOI: https://doi.org/10.1080/15481603.2019.1690780
T. Busker, B. van den Hurk, H. de Moel, M. van den Homberg, C. van Straaten, R. A. Odongo, & J. C. J. H. Aerts, Predicting Food‐Security Crises in the Horn of Africa Using Machine Learning. Earth’s Future, 12 (2024). https://doi.org/10.1029/2023EF004211. DOI: https://doi.org/10.1029/2023EF004211
M. Noorunnahar, A. H. Chowdhury, & F. A. Mila, A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PLOS ONE, 18 (2023) e0283452. https://doi.org/10.1371/journal.pone.0283452. DOI: https://doi.org/10.1371/journal.pone.0283452
A. Arshad, J. Zulfiqar, M. H. Zaib, A. Khan, & M. J. Khan, Mapping socioeconomic conditions using satellite imagery: A computer vision approach for developing countries. Journal of Economy and Technology, 1 (2023) 144–163. https://doi.org/10.1016/j.ject.2023.11.001. DOI: https://doi.org/10.1016/j.ject.2023.11.001
Y. Peng, Z. Yang, Z. Zhang, & J. Huang, A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package. Agronomy, 14 (2024) 421. https://doi.org/10.3390/agronomy14030421. DOI: https://doi.org/10.3390/agronomy14030421
M. Lamichhane, S. Mehan, & K. R. Mankin, Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities. Remote Sensing, 17 (2025) 2397. https://doi.org/10.3390/rs17142397. DOI: https://doi.org/10.3390/rs17142397
D. M. Johnson, A. Rosales, R. Mueller, C. Reynolds, R. Frantz,
A. Anyamba, E. Pak, & C. Tucker, USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses? Remote Sensing, 13 (2021) 4227. https://doi.org/10.3390/rs13214227. DOI: https://doi.org/10.3390/rs13214227
J. Sun, L. Di, Z. Sun, Y. Shen, & Z. Lai, County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model. Sensors, 19 (2019) 4363. https://doi.org/10.3390/s19204363. DOI: https://doi.org/10.3390/s19204363
S. Sah, D. Haldar, R. Singh, B. Das, & A. S. Nain, Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models. Scientific Reports, 14 (2024) 21674. https://doi.org/10.1038/s41598-024-72624-4. DOI: https://doi.org/10.1038/s41598-024-72624-4
M. R. da Silva, N. A. Streck, J. C. Cera, A. J. Duarte Junior,
G. G. Ribas, I. G. Rossato, L. D. Meus, V. F. Pereira, I. B. Pilecco, R. P. Benedetti, F. Tonetto, & A. J. Zanon, Forecasting the rice yield in Rio Grande do Sul using the SimulArroz model. Pesquisa Agropecuária Brasileira, 57 (2022). https://doi.org/10.1590/s1678-3921.pab2022.v57.02069. DOI: https://doi.org/10.1590/s1678-3921.pab2022.v57.02069
A. Anand, R. Imasu, S. K. Dhaka, & P. K. Patra, Domain Adaptation and Fine-Tuning of a Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using High-Resolution Sentinel-2 Observations: A Case Study of Punjab, India. Remote Sensing, 17 (2025) 974. https://doi.org/10.3390/rs17060974. DOI: https://doi.org/10.3390/rs17060974
M. A. Abubakar, B. E. Okpe, & M. Audu, Predicting Food Price Trends in Nigeria Using Advanced Machine Learning Techniques: LSTM and XGBoost. International Journal of Research and Innovation in Social Science, IX (2025) 3228–3237. https://doi.org/10.47772/IJRISS.2025.905000251. DOI: https://doi.org/10.47772/IJRISS.2025.905000251
R. K. Paul, Md. Yeasin, P. Kumar, P. Kumar, M. Balasubramanian,
H. S. Roy, A. K. Paul, & A. Gupta, Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. PLOS ONE, 17 (2022) e0270553. https://doi.org/10.1371/journal.pone.0270553. DOI: https://doi.org/10.1371/journal.pone.0270553
A. Apicella, F. Isgrò, & R. Prevete, Don’t push the button! Exploring data leakage risks in machine learning and transfer learning. Artificial Intelligence Review, 58 (2025) 339. https://doi.org/10.1007/s10462-025-11326-3. DOI: https://doi.org/10.1007/s10462-025-11326-3
H. Tyralis & G. Papacharalampous, A review of predictive uncertainty estimation with machine learning. Artificial Intelligence Review, 57 (2024) 94. https://doi.org/10.1007/s10462-023-10698-8. DOI: https://doi.org/10.1007/s10462-023-10698-8
H. S. Nogay, N. H. Nogay, & H. Adeli, Image‐based food groups and portion prediction by using deep learning. Journal of Food Science, 90 (2025). https://doi.org/10.1111/1750-3841.70116. DOI: https://doi.org/10.1111/1750-3841.70116
M. van Erp, C. Reynolds, D. Maynard, A. Starke, R. Ibáñez Martín,
F. Andres, M. C. A. Leite, D. Alvarez de Toledo, X. Schmidt Rivera, C. Trattner, S. Brewer, C. Adriano Martins, A. Kluczkovski,
A. Frankowska, S. Bridle, R. B. Levy, F. Rauber, J. Tereza da Silva, & U. Bosma, Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food. Frontiers in Artificial Intelligence, 3 (2021). https://doi.org/10.3389/frai.2020.621577. DOI: https://doi.org/10.3389/frai.2020.621577
X. Wang, K. Yao, Z. Huang, W. Zhao, J. Fu, P. Lou, Y. Liu, J. Hu,
Y. Li, A. Fang, & W. Chen, An artificial intelligence malnutrition screening tool based on electronic medical records. Clinical Nutrition ESPEN, 68 (2025) 153–159. https://doi.org/10.1016/j.clnesp.2025.03.178. DOI: https://doi.org/10.1016/j.clnesp.2025.03.178
P. Hosseinzadeh Kasani, J. E. Lee, C. Park, C.-H. Yun, J.-W. Jang, & S.-A. Lee, Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Frontiers in Nutrition, 10 (2023). https://doi.org/10.3389/fnut.2023.1165854. DOI: https://doi.org/10.3389/fnut.2023.1165854
J. Zhao, Y. Yu, S. Lu, Y. Huang, X. Yu, X. Mao, Y. Yu, Z. Li, &
H. Liu, Convolutional neural networks in the realm of food quality and safety evaluation: Current achievements and future prospects. Trends in Food Science & Technology, 163 (2025) 105162. https://doi.org/10.1016/j.tifs.2025.105162. DOI: https://doi.org/10.1016/j.tifs.2025.105162
E. Ahmed, Detection of honey adulteration using machine learning. PLOS Digital Health, 3 (2024) e0000536. https://doi.org/10.1371/journal.pdig.0000536. DOI: https://doi.org/10.1371/journal.pdig.0000536
L. Protopappas, D. Bechtsis, & N. Tsotsolas, IoT Services for Monitoring Food Supply Chains. Applied Sciences, 15 (2025) 7602. https://doi.org/10.3390/app15137602. DOI: https://doi.org/10.3390/app15137602
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell,
S. Ermon, & M. Burke, Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications, 11 (2020) 2583. https://doi.org/10.1038/s41467-020-16185-w. DOI: https://doi.org/10.1038/s41467-020-16185-w
M. Shahhosseini, G. Hu, & S. V. Archontoulis, Forecasting Corn Yield With Machine Learning Ensembles. Frontiers in Plant Science, 11 (2020). https://doi.org/10.3389/fpls.2020.01120. DOI: https://doi.org/10.3389/fpls.2020.01120
F. Li, T. Yigitcanlar, M. Nepal, K. Nguyen, & F. Dur, Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustainable Cities and Society, 96 (2023) 104653. https://doi.org/10.1016/j.scs.2023.104653. DOI: https://doi.org/10.1016/j.scs.2023.104653