SynthesisPest management science2026
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.
Synthesis in Pest management science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundKiwifruit 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.
resultsIn terms of disease diagnosis, the disease recognition model based on ResNet 18 achieved an accuracy of 98.70% on a dataset of 9702 live images, meets the routine diagnostic needs. In terms of screening for efficient post-harvest prevention and control measures, the meta-analysis results showed that the treatment of soaked in MT solution for 3 min and stored at 22-24 °C with a relative humidity of 85%-95% (E1/E2), and the treatment of sprayed with Bacillus tequilensis KXF 6501 fertilization broth (1 × 10
conclusionThe 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.
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