Recent Scope for AI in the Food Production Industry Leading to the Fourth Industrial Revolution

In today's situation, Artificial intelligence and computer vision collectively join together to analyze the big data obtained from predicted models. The role of AI in the agri-based food industry helps the stakeholders to access and monitor the supply chain. The phenomenon of applying AI and computer vision in the food industry would improve the entire operations. This research paper tries to provide an assisting model for farmers in food-processing and agriculture through the state-of-the-art method. Several concepts related to sustainability in food processing have been estimated through machine learning, and the deep learning model as a worldwide concept. The demand for the usage of AI and computer vision in the Ag-TECH industry has increased which impacts sustainable food production to feed the future. Certain implications have been suggested for real-time monitoring of the farming process, politics behind sustainable food production, and investment which is the main game-player in the present situation. The 4th Industrial Revolution [IR-4.0] was ushered in by the deployment of computer vision and AI in the food business, with computer vision robotics playing a crucial role in ensuring sustainable food production.

Fiscal policies to promote environmentally sustainable food production and consumption: A scoping review protocol

The objective of this scoping review is to examine food taxes and subsidies and their environmental outcomes by incorporating evidence from the peer-reviewed literature and evidence from the non-peer-reviewed or ‘grey’ literature, including policy documents/briefs, research reports, and white papers. The scoping review questions are:1. What food taxes and subsidies have been studied in the context of environmentally sustainable food production or consumption?2. What types of environmental outcomes were reported?3. At what various levels (e.g., food production-, food outlet-, consumer-level) were the policies implemented?This is the final version of the scoping review protocol, published as a preprint to create full transparency about the scoping review process.

Digital Agriculture Infrastructure in the USA and Germany

The USA and Germany have compared the issues that surround the adoption of digital technology on the farm that will foster more environmentally sustainable food production/processing systems. Both countries lack robust broadband internet pathways to foster the adoption of these technologies. The problem is currently relevant to making this data technology available on every farm and field. The implementation of this infrastructure is even more important as society demands more and more information on the product and production process of agriculture and industry.

Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems

A novel total ensemble (TE) algorithm was developed and compared with random forest optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and Bayesian additive regression tree (BART) algorithms to predict numerous soil health indicators in soils with diverse climate-smart land uses at different soil depths. The study investigated how land-use practices affect several soil health indicators. Good predictions using the ensemble method were obtained for total carbon (R2 = 0.87; RMSE = 0.39; RPIQ = 1.36 and RPD = 1.51), total nitrogen (R2 = 0.82; RMSE = 0.03; RPIQ = 2.00 and RPD = 1.60), and exchangeable bases, m3. Cu, m3. Fe, m3. B, m3. Mn, exchangeable Na, Ca (R2 > 0.70). The performances of algorithms were in order of TE > Cubist > BART > PLS > GBM > RFO. Soil properties differed significantly among land uses and between soil depths. In Kenya, however, soil pH was not significant, except at depths of 45–100 cm, while the Fe levels in Tanzanian grassland were significantly high at all depths. Ugandan agroforestry had a substantially high concentration of ExCa at 0–15 cm. The total ensemble method showed better predictions as compared to other algorithms. Climate-smart land-use practices to preserve soil quality can be adopted for sustainable food production systems.

Food Security and Transition towards Sustainability

In the light of linkages in various scales and targets, the complex and nuanced design of the sustainable development goals (SDG) raises more challenges in their implementation on the ground. This paper reviewed 25 food security indicators, proposed improvements to facilitate operationalization, and illustrated practical implementation. The research focused on three essential blind spots that arise from the potential interactions between sustainable food production, consumption, and domestic material consumption (DMC). Projection of latent structure regression was applied to link food security and sustainable development goals. Findings revealed that the key target in reducing trade-offs was the integration of DMC with sustainable food production and consumption. DMC was positively correlated with the creation of coherent SDG strategies and sustainable food security. Practical implications were discussed by highlighting how to achieve food security across contrasting development contexts and the challenges of addressing the links between targets and indicators within and beyond SDGs 2 and 12. The results are useful for setting a proper strategy for sustainable production and consumption that can improve the efficient use of resources in the eight Central European countries.