Reliability assessment of agricultural sensors evaluated through algal coverage in hydroponic tomato production systems

In modern agricultural systems, hydroponics represents a crucial advancement in integrating digital technologies and precision farming practices for sensor-mediated cultivation. These systems employ continuous environmental monitoring to enhance operational efficiency and promote plant growth. However, environmental factors and technical issues can undermine data integrity because of faulty sensor performance. Detecting sensor malfunctions during cultivation is challenging. This study investigated whether algal coverage patterns on sensor surfaces could explain observed variations in sensor-recorded environmental parameters in rockwool-based hydroponic tomato systems. In a controlled greenhouse setting, 117 environmental sensors continuously monitored root-zone temperature, relative humidity, pH, and electrical conductivity (EC). Despite uniform conditions, substantial sensor data variation was observed. Post-cultivation analysis revealed marked differences in algal coverage across sensor surfaces. We hypothesized that algae coverage ratios reflect differential nutrient solution distribution within rockwool substrate, potentially explaining sensor data variation. After 3 months, 39 sensors were categorized according to their algal coverage: 22 sensors exhibited high colonization (≥ 90% coverage) and 17 displayed minimal coverage (< 10%). The sensors with significant algal coverage presented markedly elevated substrate relative humidity (85.6% vs. 41.9%) and EC values (0.77 dS/m vs. 0.33 dS/m) than those with minimal coverage. The tomato productivity metrics did not differ significantly, implying potential biological adaptations to water deficiency or hydrodynamic characteristics of the rockwool. These findings suggest that quantifying algal growth may indirectly reflect environmental parameters, particularly relative humidity and EC, while serving as an inferential validation indicator of sensor data reliability in hydroponic systems. This study addresses the crucial challenges in achieving precise environmental monitoring in advanced agricultural systems, optimizing resource utilization, and improving crop production efficiency.