RT Journal Article
T1 Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0
A1 Martinho, Vítor João Pereira Domingues
A1 Cunha, Carlos Augusto da Silva
A1 Pato, Maria Lúcia
A1 Costa, Paulo Jorge Lourenço
A1 Sánchez Carreira, María del Carmen
A1 Georgantzís, Nikolaos
A1 Rodrigues, Raimundo Nonato
A1 Coronado, Freddy
K1 Literature review
K1 Bibliometric analysis
K1 Food 4.0
K1 Industry 4.0
K1 Climate-Smart Agriculture
AB Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 ≈ 0.99 and RMSE ≈ 1%).
PB MDPI
SN 2076-3417
YR 2022
FD 2022
LK https://hdl.handle.net/10347/41059
UL https://hdl.handle.net/10347/41059
LA eng
NO Martinho, V. J. P. D., Cunha, C. A. d. S., Pato, M. L., Costa, P. J. L., Sánchez-Carreira, M. C., Georgantzís, N., Rodrigues, R. N., & Coronado, F. (2022). Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0. Applied Sciences, 12(22), 11828. https://doi.org/10.3390/app122211828
NO This work is funded by National Funds through the FCT-Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/00681/2020. This research is also funded by the Enovo company. This study was carried out under the international project “Agriculture 4.0: Current reality, potentialities and policy proposals” (CERNAS-IPV/2022/008).
DS Minerva
RD 24 abr 2026