Baek Insuck, Lim Seunghyun, Weerarathne Visna, Lee Dongho, Botkin Jacob, Kirubakaran Silvas, Park Sunchung, Kim Moon S, Meinhardt Lyndel W, Ahn Ezekiel
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, 20705, USA.
Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, 20705, USA.
Commun Biol. 2025 Apr 4;8(1):554. doi: 10.1038/s42003-025-08019-6.
Leaf development and the coordinated formation of its key components is a fundamental process driving plant growth and adaptation. In tropical species like cacao, flush growth, a period of rapid leaf expansion, is particularly dependent on the optimized spatial patterns of chloroplasts and stomata. In this study, we investigated the patterns in cacao leaves during growth Stage C, a phase marked by rapid chlorophyll accumulation. Microscopic image data revealed significant acropetal variations in the size and density of chloroplast clusters and stomata, with the largest values found near the leaf base, mirroring the leaf greenness gradient. These findings suggest a coordinated developmental sequence between chloroplasts, stomata, and leaf ontogeny. A Support Vector Machine (SVM) model successfully classified distinct leaf regions based on these morphological features (>80% accuracy), highlighting the potential of machine learning applications in this area. Our results provide novel insights into the spatial coordination of chloroplast and stomatal development during cacao leaf maturation, offering a foundation for future research on flush growth optimization. To the best of our knowledge, this is the first report that combines microscopic data and machine learning analysis to investigate the leaf developmental process at stage C in cacao.
叶片发育及其关键组成部分的协同形成是驱动植物生长和适应的一个基本过程。在可可等热带物种中,快速生长阶段,即叶片迅速扩张的时期,尤其依赖于叶绿体和气孔的优化空间模式。在本研究中,我们调查了可可叶片在生长阶段C(以叶绿素快速积累为标志的阶段)的模式。微观图像数据显示,叶绿体簇和气孔的大小及密度存在显著的向顶变化,在叶基部附近的值最大,这与叶片的绿色梯度一致。这些发现表明叶绿体、气孔和叶片个体发育之间存在协同发育序列。一个支持向量机(SVM)模型基于这些形态特征成功地对不同的叶片区域进行了分类(准确率>80%),突出了机器学习在该领域应用的潜力。我们的结果为可可叶片成熟过程中叶绿体和气孔发育的空间协调提供了新的见解,为未来关于快速生长优化的研究奠定了基础。据我们所知,这是第一份结合微观数据和机器学习分析来研究可可生长阶段C叶片发育过程的报告。