Resource management system for optimizing production efficiency in the wine sector

• Main Author: Branquinho , Rodrigo Manuel Ferreira
• Publication Date: 2024
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.22/26807
• topic: Data processing; Evapotranspiration; Renewable energies; Resource optimization; Smart irrigation; Sustainable agriculture; Vineyard management; Agricultura sustentável; Energias renováveis; Rega inteligente; Evapotranspiração; Gestão de vinhas; Otimização de recursos; Processamento de dados

Being agriculture an essential activity sector for the survival and prosperity of humanity, it is fundamental to use sustainable technologies in this field. With this in mind, statistical data is analyzed regarding the food price rise and sustainable development indicators. It is determined that one of the main factors that influences agriculture’s success is the soil’s characteristics, namely in terms of moisture and nutrients. In this regard, irrigation processes have become indispensable, and their technological management brings countless economic advantages. Like other branches of agriculture, the wine sector needs an adequate concentration of nutrients and moisture in the soil to provide the most efficient results. Given these facts, the use of renewable energies is an important aspect of this study, which also synthesizes the main irrigation methods and examines the importance of evaluating the evapotranspiration of crops. Furthermore, the control of irrigation processes and the implementation of resource management models are of utmost importance to allow maximum efficiency and sustainability in this field. However, there is a lack of a resource management model that maximizes production efficiency with the largest number of compatible technologies. Therefore, a modular intelligent system is developed, which utilizes data obtained from multiple sources, such as weather conditions, renewable energy and electricity markets. Divided into four key modules, the first module embraces data diversity as an important factor, so the system can adapt to multiple inputs and user definitions. The second module is responsible for determining vineyard water losses through evapotranspiration, and the third one calculates the water requirements for irrigation purposes. The last module uses a supervised machine learning algorithm to achieve optimal energy consumption information. To validate and analyse the model performance, multiple results are provided, considering various realistic scenarios. By adapting the system to a specific vineyard, vital data is acquired and farms can make informed decisions, specially regarding energy and water resources. With a good design and positive results, the system enables a boost in the environmental sustainability of the wine sector operations and enhances productivity of vineyards.