Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0

Abstract

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%).