Overhead Agrivoltaic Systems Delay Apple Ripening and Influence Maturation Patterns

Agrivoltaic systems, combining solar energy generation with agricultural activities, offer a sustainable approach to maximising land efficiency. However, these systems can present challenges, such as potential shading effects that may impact fruit quality or crop yields. This study evaluated the impact of overhead agrivoltaic systems on apple ( Malus domestica L. cv. Gala) ripening and maturation patterns in a temperate orchard near Lake Constance, Germany. Experiments compared apples grown under conventional conditions (control) with those under agrivoltaic setups equipped with semi-transparent photovoltaic panels utilizing spatially distributed cells for 40% light transparency installed with a 70% ground-coverage ratio. Key metrics, including fruit diameter, length, volume, and BBCH phenology stages, were monitored throughout the 2024 growing season. An IoT-capable fixed RGB camera system captured daily images, and a machine learning algorithm assessed ripeness based on colour changes. Results indicated that apples under agrivoltaic conditions experienced a significant delay in ripening, reaching full maturity approximately 12 days later than the control group. On September 13 (harvest), no significant differences were found in mean length (67.54 mm for agrivoltaic apples and 70.05 mm for control apples), while the diameter of agrivoltaic apples was significantly smaller (65.59 mm versus 70.98 mm), indicating slightly smaller dimensions under shaded conditions. Fruit volume and weight were approximately 16% lower under agrivoltaic conditions, averaging 161.16 cm³ (138.6 g) versus 191.58 cm³ (164.8 g) in the control. The delayed maturation is attributed to reduced sunlight due to shading from the solar panels, affecting physiological processes essential for ripening. These findings indicate that overhead agrivoltaic systems can significantly delay apple phenology and fruit maturation. Depending on the agricultural goals, the desired harvest timing and the cultivar, this may be challenging or beneficial, e.g., if it adapts the crop against climate change impacts or other factors such as local climate conditions, latitude and geographic region, and market demand. Integrating IoT-based monitoring with machine learning enhances the precision of agricultural assessments, providing valuable data for managing the effects of agrivoltaic systems on crop development.