Greenhouse microclimates exhibit substantial spatial heterogeneity. Conventional evaluations based on average measurements cannot adequately describe the environmental conditions experienced by crops. This study developed a spatial frequency analysis method to quantify long-term temperature and relative humidity heterogeneity. This method was coupled with cucumber yield and downy mildew models to evaluate the biological consequences of environmental variability. Environmental conditions in four representative greenhouse structures were monitored using distributed sensor networks under different weather conditions. The proposed method successfully identified persistent environmental hotspots. The spatial distribution of environmental heterogeneity differed among greenhouse structures, whereas solar radiation primarily controlled its intensity. Long-season monitoring data indicate that the maximum spatial variations in temperature and relative humidity within the brick-wall solar greenhouse, the assembled greenhouse, the glass greenhouse, and the plastic multi-span greenhouse reach up to 10 °C and 46%, 8.5 °C and 46%, 12 °C and 50%, and 7 °C and 32%, respectively. The assembled solar greenhouse and plastic greenhouse exhibited higher environmental uniformity, while the brick-wall solar greenhouse and glass multi-span greenhouse showed pronounced spatial gradients. Environmental heterogeneity resulted in significant within-greenhouse differences in simulated cucumber yield, with the smallest variation occurring in the assembled solar greenhouse (5.3%) and the largest in the glass multi-span greenhouse (39.2%). Disease simulations further revealed clear spatial aggregation of cucumber downy mildew risk, with the southern region of the solar greenhouse exhibiting 17–67% higher cumulative infection risk than other positions. This study establishes an integrated framework linking greenhouse environmental heterogeneity with crop productivity and disease risk, providing a practical basis for spatially differentiated precision greenhouse management and greenhouse structural optimization.

Greenhouse microclimates exhibit substantial spatial heterogeneity, whereas conventional evaluations based on average measurements cannot adequately describe the environmental conditions experienced by crops. This study developed a spatial frequency analysis method to quantify long-term temperature and relative humidity heterogeneity and coupled it with cucumber yield and downy mildew models to evaluate the biological consequences of environmental variability. Four representative greenhouse types were monitored using distributed sensor networks under different weather conditions. The proposed method successfully identified persistent environmental hotspots. Greenhouse structure determined the spatial distribution of environmental heterogeneity, whereas solar radiation primarily controlled its intensity. Long-season monitoring data indicate that the maximum spatial variations in temperature and humidity within the brick-wall solar greenhouse, the assembled greenhouse, the glass greenhouse, and the plastic multi-span greenhouse reach up to 10℃ and 46%, 8.5℃ and 46%, 12℃ and 50%, and 7℃ and 32%, respectively. The assembled solar greenhouse and plastic greenhouse exhibited higher environmental uniformity, while the brick-wall solar greenhouse and glass multi-span greenhouse showed pronounced spatial gradients. Environmental heterogeneity resulted in significant within-greenhouse differences in simulated cucumber yield, with the smallest variation occurring in the assembled solar greenhouse (5.3%) and the largest in the glass multi-span greenhouse (39.2%). Disease simulations further revealed clear spatial aggregation of cucumber downy mildew risk, with the southern region of the solar greenhouse exhibiting 17-67% higher cumulative infection risk than other positions. This study establishes an integrated framework linking greenhouse environmental heterogeneity with crop productivity and disease risk, providing a practical basis for spatially differentiated precision greenhouse management and greenhouse structural optimization.