SolarFertigation: A Unified Cloud Platform for Smart Fertigation and Agrivoltaic System Integration

SolarFertigation is an intelligent fertigation system powered by solar energy. It enables precise, adaptive management of fertigation processes based on real-time environmental, agronomic, and operational data [1,2]. Developed to support sustainable and innovative agriculture, the system is also designed for effective integration within agrivoltaic contexts. Flexibility is ensured by the platform’s capacity to seamlessly incorporate smart farming sensors and machines, both physically (hardware) and digitally, through a modular and scalable software architecture. The new cloud-based architecture introduces advanced automation models that adjust fertigation schedules dynamically according to crop phenology, weather conditions, soil characteristics, topography, and machine status [3]. The system tracks detailed metrics such as nutrient composition and dosage (g), distributed water volumes (L), crop stages, and field operations across multiple levels (field, zone, crop). These data not only improve fertigation efficiency but also support compliance with the Italian Guidelines for Agrivoltaic Systems (MASE), including metrics such as LAOR – Land Area Occupation Ratio, limited to 40% photovoltaic coverage [4]. These national directives could serve as a foundation for international standards, aligning with the recommendations of the International Energy Agency (IEA PVPS Task 13) [5]. In this framework, SolarFertigation acts as a strategic enabling technology, making these principles operational and scalable. Additionally, the cloud architecture supports future integration of photovoltaic production monitoring, including energy yield, inverter health, and solar panel orientation [6]. Field validation of the system demonstrated its effectiveness in terms of LoRaWAN signal coverage and data transmission reliability, even in the presence of structural obstacles. Moreover, experimental field campaigns revealed a significant reduction in solar radiation and light intensity indices beneath the agrivoltaic modules, with implications on crop performance that will be discussed in the following sections, including a general reduction in yield under photovoltaic panels, which in some cases exceeded 80% compared to open-field conditions. This paper presents the system’s architecture and outlines future scenarios for intelligent and scalable farm management aligned with ecological transition objectives.