The combined use of residual network and meta‐analysis for pre‐harvest diagnosis of kiwifruit gray mold disease and screening of efficient post‐harvest control measures

BACKGROUND: Kiwifruit gray mold (caused by Botrytis cinerea) causes severe post-harvest losses. Traditional manual disease identification is inefficient, and post-harvest control measures lack systematic comparison. The aim of this study is to combine the residual network ResNet 18 model (ResNet 18-A) using Adam optimizer (which can maintain the adaptive learning rate of each parameter separately) with meta-analysis methods for preharvest diagnosis of kiwifruit gray mold and screening of effective post-harvest control measures, providing reference for scientific management of kiwifruit orchards. RESULTS: ) has a good comprehensive effect on various kiwifruit quality indicators. CONCLUSION: The combination of ResNet 18 and meta-analysis can effectively improve the management efficiency of kiwifruit gray mold. Both models have great potential in achieving efficient disease diagnosis and targeted post-harvest prevention and control measures selection, which can help optimize disease management strategies for horticultural crops. © 2026 Society of Chemical Industry.