Metabolic mapping for precision grape maturation: Application of a tomography-like method for site-specific management

IF 6.3 Q1 AGRICULTURAL ENGINEERING
Renan Tosin , Leandro Rodrigues , Maria Santos-Campos , Igor Gonçalves , Catarina Barbosa , Filipe Santos , Rui Martins , Mario Cunha
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Abstract

This study demonstrates the application of a tomography-like (TL) method to monitor grape maturation dynamics over two growing seasons (2021–2022) in the Douro Wine Region. Using a Vis-NIR point-of-measurement sensor, which employs visible and near-infrared light to penetrate grape tissues non-destructively and provide spectral data to predict internal composition, this approach captures non-destructive measurements of key physicochemical properties, including soluble solids content (SSC), weight-to-volume ratio, chlorophyll and anthocyanin levels across internal grape tissues - skin, pulp, and seeds - over six post-veraison stages. The collected data were used to generate detailed metabolic maps of maturation, integrating topographical factors such as altitude and NDVI-based (normalised difference vegetation index) vigour assessments, which revealed significant (p < 0.05) variations in SSC, chlorophyll, and anthocyanin levels across vineyard zones. The metabolic maps generated from the TL method enable high-throughput data to reveal the impact of environmental variability on grape maturation across distinct vineyard areas. Predictive models using random forest (RF) and self-learning artificial intelligence (SL-AI) algorithms showed RF’s robustness, achieving stable predictions with R² ≥ 0.86 and MAPE ≤ 33.83 %. To illustrate the TL method’s practical value, three hypothetical decision models were developed for targeted winemaking objectives based on SSC, chlorophyll in the pulp, and anthocyanin in the skin and seeds. These models underscore the TL method’s ability to support site-specific management (SSM) by providing actionable agricultural practices (e.g. harvest) into vineyard management, guiding winemakers to implement tailored interventions based on metabolic profiles rather than only cultivar characteristics. This precision viticulture (PV) approach enhances wine quality and production efficiency by aligning vineyard practices with specific wine quality goals.

Abstract Image

精确葡萄成熟的代谢图谱:应用层析成像方法进行特定地点的管理
本研究展示了应用层析成像(TL)方法来监测杜罗葡萄酒产区两个生长季节(2021-2022)的葡萄成熟动态。使用Vis-NIR测量点传感器,该传感器利用可见光和近红外光非破坏性地穿透葡萄组织,并提供光谱数据来预测内部成分,该方法捕获关键物理化学特性的非破坏性测量,包括可溶性固形物含量(SSC),重量体积比,叶绿素和花青素水平跨越葡萄内部组织-皮,果肉和种子-在六个版本后阶段。收集到的数据用于生成详细的成熟代谢图,整合地形因素,如海拔和基于ndvi(归一化植被指数)的活力评估,揭示了显著的(p <;0.05) SSC、叶绿素和花青素水平在葡萄园区之间的差异。由TL方法生成的代谢图谱使高通量数据能够揭示不同葡萄园区环境变化对葡萄成熟的影响。采用随机森林(RF)和自学习人工智能(SL-AI)算法建立的预测模型显示RF具有鲁棒性,预测稳定,R²≥0.86,MAPE≤33.83%。为了说明TL方法的实用价值,基于SSC、果肉中的叶绿素、果皮和种子中的花青素,为有针对性的酿酒目标开发了三个假设决策模型。这些模型强调了TL方法支持特定地点管理(SSM)的能力,通过为葡萄园管理提供可操作的农业实践(例如收获),指导酿酒师根据代谢特征而不仅仅是品种特征实施量身定制的干预措施。这种精确的葡萄栽培(PV)方法通过将葡萄园实践与特定的葡萄酒质量目标结合起来,提高了葡萄酒的质量和生产效率。
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