Estimation of Cucumber Fruit Yield Cultivated Under Different Light Conditions in Greenhouses

In recent years, an increase in the frequency of low-sunlight conditions due to climate change has resulted in a decline in the yield and quality of crops for greenhouse farmers, leading to significant challenges in maintaining optimal plant growth. The crop growth model can be used to predict changes in cucumber yield in response to variations in sunlight, which can help efficiently address sunlight shortages. The objective of this study was to improve and validate the model for predicting cucumber yield under different light environment conditions, including shading and supplemental lighting. The model comprises three steps: LAI prediction, daily assimilate yield prediction, and fruit yield prediction, each of which involves modifying the coefficients applied to suit the cucumber cultivar and environment condition. The improved model demonstrated a high degree of accuracy in predicting cucumber yields in the control and low-sunlight treatments (10, 20, and 30% shading), with a coefficient of determination (R2) > 0.98. When supplemental lighting was incorporated into the control and shading treatments, the accuracy of the improved model in predicting cucumber yield was also high, with a coefficient of determination (R2) > 0.99. The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting. The findings of this study indicate that the improved cucumber yield prediction model can be applied to assess the efficacy of yield reduction in low-sunlight conditions and the potential for yield enhancement through supplemental lighting.