Pathogen-specific stomatal responses in cacao leaves to Phytophthora megakarya and Rhizoctonia solani.

Abstract

Cacao is a globally significant crop, but its production is severely threatened by diseases, particularly Black Pod Rot (BPR) caused by Phytophthora spp. Understanding plant-pathogen interactions, especially stomatal responses, is crucial for disease management. Machine learning offers a powerful, yet largely untapped, approach to analyze and interpret complex plant responses in plant biology and pathology, particularly in the context of plant-pathogen interactions. This study explores the use of machine learning to analyze and interpret complex stomatal responses in cacao leaves during pathogen interactions. We investigated the impact of the black pod rot pathogen (Phytophthora megakarya) and a non-pathogenic fungus (Rhizoctonia solani) on stomatal aperture in two cacao genotypes (SCA6 and Pound7) under varying light conditions. Image analysis revealed diverse stomatal responses, including no change, opening, and closure, that were influenced by the interplay of genotype, pathogen isolate, and light conditions. Notably, SCA6 exhibited stomatal opening in response to P. megakarya specifically under a 12-hour light/dark cycle, suggesting a light-dependent activation of pathogen virulence factors. In contrast, Pound7 displayed stomatal closure in response to both P. megakarya and R. solani, indicating the potential recognition of conserved Pathogen-Associated Molecular Patterns (PAMPs) and a broader defense response. To further analyze these interactions, we employed machine learning techniques to predict stomatal area size. Our analysis identified key morphological features, with size-related traits being the strongest predictors. Shape-related traits also played a significant role when size-related traits were excluded from the prediction. This study demonstrates the power of combining image analysis and machine learning for discerning subtle, multivariate traits in stomatal dynamics during plant-pathogen interactions, paving the way for future applications in high-throughput disease phenotyping and the development of resistant crop varieties.

Plant phenotyping relevance

カカオ葉の気孔開度を画像解析で測定し、機械学習で気孔面積を予測する手法が研究の中心であり、病原体応答の表現型解析および将来の高スループット病害フェノタイピングへの応用を実証している。

Code and data availability

The article describes stomatal image analysis and machine learning on cacao leaves, and mentions Supplementary Data S1 containing stomatal measurements, but no public repository, dataset URL, or code availability statement appears in the supplied blocks. The only URLs present are ROR affiliation identifiers and the CC-

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