This paper reveals two of the challenges faced by Romania and proposes a sustainable and simple solution for its wine industry. First, substantial areas with vineyards that may produce qualitative wine, and second, the very low digitalization rate of industrial sectors. More precisely, this work proposes a solution for digitalizing the fermentation process of white wine, allowing it to be adapted for other control techniques (i.e., knowledge-based systems, intelligent control). Our method consists of implementing a pre-trained multi-layer perceptron neural network, using genetic algorithms capable of predicting the concentration of alcohol and the amount of substrate at a certain point in time that starts from the initial configuration of the fermentation process. The purpose of predicting these process features is to obtain information about status variables so that the process can be automatically driven. The main advantage of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. After comprehensive simulations using experimental data obtained from previous fermentation processes, we concluded that a configuration that is close to the optimal one, for which the prediction accuracy is high, is a neural network (NN) having an input layer with neurons for temperature, time, initial substrate concentration, and the biomass concentration, a hidden layer with 10 neurons, and an output layer with 2 neurons representing the alcohol and substrate concentration, respectively. The best results were obtained with a pre-trained NN, using a genetic algorithm (GA) with a population of 50 individuals for 20 generations, a crossover probability of 0.9, and a probability of mutation of 0.5 that uniformly decreases depending on the generations, based on a beta coefficient of 0.3 and an elitist selection method. In the case of a data set with a larger number of variables, which also contains data regarding pH and CO₂, the prediction accuracy is even higher, leading to the conclusion that a larger data set positively influences the performance of the neural network. Furthermore, methods based on artificial intelligence applications like neural networks, along with various heuristic optimization methods such as genetic algorithms, are essential if hardware sensors cannot be used, or if direct measurements cannot be made.

One goal of specialists in food processing is to increase production efficiency in accordance with sustainability by optimising the consumption of raw food materials, water, and energy. One way to achieve this purpose is to develop new methods for process monitoring and control. In the winemaking industry, there is a lack of procedures regarding the common work based on knowledge acquisition and intelligent control. In the present article, we developed and tested a knowledge-based system for the alcoholic fermentation process of white winemaking while considering the main phases: the latent phase, exponential growth phase, and decay phase. The automatic control of the white wine’s alcoholic fermentation process was designed as a system on three levels. Level zero represents the measurement and adjustment loops of the bioreactor. At the first level of control, the three phases of the process are detected functions of the characteristics of the fermentation medium (the initial substrate concentration, the nitrogen assimilable content, and the initial concentration of biomass) and, thus, functions on the phase’s duration. The second level achieves the sequence supervision of the process (the operation sequence of a fermentation batch) and transforms the process into a continuous one. This control level ensures the quality of the process as well as its diagnosis. This software application can be extended to the industrial scale and can be improved by using further artificial intelligence techniques.

Our research provides solutions to alleviate the economic problems currently plaguing our planet that are responsible for the decline of its ecological systems. Our motivation is the need to identify elements that will encourage and accelerate the transition from a linear to a circular economic model, raising awareness of the limited nature of resources and the major pressures exerted by climate change and population growth. Our paper highlights the implications of strategic thinking, i.e., strategic management, in the development and promotion of the circular economy, including the concept of sustainability, in the agri-food sector. We propose strategic options based on information from our secondary analysis of statistical data and relevant literature, e.g., from PESTEL, SWOT, and DPSIR diagnostic models, for integrating resource flows into circular processes, which are meant to reduce resource consumption and minimise waste. Our paper elaborates on an integrated and dynamic model for the transition from a linear to a circular economic model; furthermore, we perform further research to create appropriate frameworks for elaborating on and implementing the most relevant policy options to accelerate this transition process.

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO₂ as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO_2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.

The sustainable development of the Romanian countryside follows strategies outlined in the Common Agricultural Policy. This policy aims to ensure the sustainability of agricultural and non-agricultural businesses, improving inhabitants’ quality of life. Achieving sustainable development is the objective of every rural locality in Romania. This is accomplished by determining the state of endogenous resources and identifying potential conservation and sustainable exploitation alternatives by developing relevant strategic options. The purpose of this research is to develop relevant strategic options for the sustainable rural development of Gușoeni Commune, Vâlcea County, by using the case study methodology and involving stakeholders and community members. In this study, we develop an integrated and dynamic model based on information from a secondary analysis of statistical data and the specialized literature, with the help of the PESTEL (political, economic, social, technological, ecological, legislative), SWOT (strengths, weaknesses, opportunities, and threats), problem tree, objective tree, and DFPSIR (drivers, pressure, status, impact, response) diagnostic models.

This paper is based on the modernization of work processes in agriculture by ensuring the efficient management of land and equipment and the acquisition of inputs given the specific natural variation in environmental conditions. Specifically, the paper highlights research from a dual perspective, descriptive and explanatory, according to the methodology of the case study conducted in the field of the agricultural enterprise SC AgriConsorțium SRL, located in the S–W of Romania, by adopting the spatial technology for the aerial monitorization of agricultural crops and for signalizing, in real time, the changes and vulnerabilities of the agroecosystem in order to function and develop sustainably. The research aims to promote spatial technologies to monitor crop growth resources, crop vegetation conditions, the real-time signaling of changes, and vulnerabilities in the agroecosystem. The research study’s results highlight the role of the aerial monitoring of crops and rapid signaling of changes in the agroecosystem, such as vegetation conditions, plant density, quality of applied work, and the destruction of crops by overgrazing for the rapid and relevant assessment of affected areas and damage. The case study of the paper is a modern, innovative, and sustainable tool for digitizing agricultural enterprises to obtain accurate information on changes in the agroecosystem and to adopt a geographical information system for recording and managing data specific to cultivated areas and their use in providing studies and reports necessary for state institutions, respectively, in order to support and guide the decision-making process. The obtained results are the basis for future research on the interpretation and use of information obtained by drones.