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Defense mechanisms of kiwifruit against Corynespora cassiicola and predictive model for estimating resistance levels.

Plant disease · 25 May 2026 · 10.1094/pdis-01-26-0149-re

Abstract

Brown spot disease, caused by Corynespora cassiicola , poses a major threat to kiwifruit production, leading to severe defoliation, nutrient loss, and significant economic damage. This study assessed 25 kiwifruit germplasm accessions and found 64% exhibited resistance, including three highly resistant (HR) cultivars; most commercial and wild germplasm accessions were moderately resistant (MR) or highly susceptible (HS). Three cultivars representing different resistance levels - HR 'Longshan' (LS), MR 'Jinyan' (JY), and HS 'Hongyang' (HY) - were selected for mechanistic analysis. Resistant types showed stronger structural defenses: 64.55% lower stomatal density, 60.05% more trichome branching, and 52.28% higher epicuticular wax content than susceptible ones. These traits delayed appressorium formation by 12 hours and hindered penetration peg development. After infection, resistant plants activated rapid immune responses-ROS burst, hypersensitive reaction, and extensive lignin deposition. In LS, four defense enzyme activities rose 12-48 hours earlier than in HY and reached 1.12-1.57 times higher levels. Six defense-related genes were significantly up-regulated within 48 hours post-inoculation (hpi). Stepwise regression of 22 variables identified five key predictors of resistance: stomatal density, lesion diameter at 120 hpi (cm), average Phenylalanine Ammonia-Lyase gene expression (0, 4 and 8 hpi), peroxidase enzyme average activity (36, 48 and 72 hpi), and H 2 O 2 accumulation average area (12, 24 and 36 hpi). A model based on these achieved 94.24% accuracy (R² = 0.95) in field validation, offering a reliable tool for evaluating kiwifruit resistance.

Plant phenotyping relevance

キウイフルーツの病害抵抗性を推定する予測モデルを開発し、圃場で検証しており、植物の病害状態を評価する手法が中心的です。

titlepredictive model for estimating resistance levels
abstractStepwise regression of 22 variables identified five key predictors of resistance
abstractA model based on these achieved 94.24% accuracy (R² = 0.95) in field validation, offering a reliable tool for evaluating kiwifruit resistance.

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