The increase of the frequency and strength of extreme meteorological events related to climate change is a trend that will characterize the upcoming years. Droughts are part of this scenario, resulting in a great threat to one of the most crucial human activities, agriculture. With the decrease of availability of water, its optimal management will be a critical aspect for the survival and success of farmers’ business. Vines are one of the most valuable crops suffering from the alteration of regular seasonal cycles, which represents a dramatic threat for this economic sector.
AIS4SIA project tries to deal with this problem with the aid of technology and artificial intelligence. One effective way to monitor the health status of vineyards is offered by Crop Water Stress Index (CWSI), a quantitative measure of the need of water of a plant. We study, design and implement an intelligent autonomous system to measure the spatial and temporal distribution of CWSI on a crop field, offering a valuable instrument to have a precise insight into the health of vineyards and define targeted interventions to recover the optimal water conditions needed to achieve desired characteristics of the production. Furthermore, geospatial and historical analysis of CWSI can be performed to gain insight into the characteristics of the crop field and infer connections with the productivity and harvest.
The proposed solution is a system composed of two main parts: the sensing module that collects climatic data throughout the vineyard and the computational engine that calculates the value of the CWSI based on such data and generates a georeferenced heatmap. Its potentialities have been tested in a data collection session in a vineyard owned by Azienda Agricola BalladorePallieri through a low-cost prototype composed of miniaturized sensors mounted on a Raspberry Pi that we designed and assembled ad-hoc for the purpose. This on-field campaign helped us to verify the validity of the concept and its implementation to collect climatic variables and elaborate the data into real CWSI heatmaps. Moreover, it contributed to highlight the areas of improvement of the design, showing its advantages and criticalities.
The system well fits the current state of society responding to one of the most urgent challenges: climate change. The system provides value as a data monitoring tool able to inform the farmers on the hydration condition of the field almost at real-time with great level of geographic detail, enabling a wise water management and boosting plants care. The possibility to control the production in terms of both quality and quantity results into a reduction of risks and a strategic advantage. Moreover, it increases the resilience of the economic system to climate change, encouraging the penetration of advanced technological solution into this fragile sector.
Principal Academic Tutor
Stefano Mariani, Politecnico di Milano, Building and Environmental Engineering
Valter Carvelli, Politecnico di Milano, Dept. of Achitecture, Building and Builded Environment Engineering
Chiara Corbari, Politecnico di Milano, Dept. of Building and Environmental Engineering
Stefano Invernizzi, Politecnico di Torino, Dept. of Structural, Building and Geotechnical Engineering
Gabriele Balducci, Politecnico di Milano, Telecommunication Engineering
Alice Ballestra, Politecnico di Milano, Design and Engineering
Pietro Brachdel Prever, Politecnico di Torino, Mechatronic Engineering
Carlo Ghiglione, Politecnico di Milano, Mathematical Engineering
Laura Mascheretti, Politecnico di Milano, Materials Engineering and Nanotechnology
Margherita Molinari, Politecnico di Milano, Building Achitecture
Giuseppe Nicoletti, Politecnico di Milano, Building Engineering