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Key message <p> Prolonged hydric deficit significantly reduces the transpiration and growth of adult avocado trees, while also accumulating soluble sugars in the leaves and reducing root volume and diameter.</p> Abstract <p>Climate change and exploitation is expected to drastically reduce water availability in central Chile by placing avocado (<i>Persea americana</i> Mill.) production at severe risk owing to the crop´s high water footprint. Current research on avocado hydric stress remains limited in scope and has typically focused exclusively on aerial plant parts of avocado, with little attention paid to belowground responses. Root phenotyping, defined as the systematic evaluation of root system architecture (RSA), offers a powerful tool for investigating stress adaptation, particularly when combined with aerial phenotyping and metabolomics. In the present study, 28-month-old avocado trees (cv. Hass) grafted onto Mexicola rootstock were grown in 55-L rhizotrons under drip fertigation and subjected to four months of water deficit (“drought stress”). Aerial phenotyping included tree height, trunk area, leaf water potential (LWP) and stomatal conductance (g<sub>s</sub>). Root phenotyping was conducted using an artificial intelligence-based deep learning convolutional neural network with RootPainter coupled with RhizoVision Explorer for quantitative trait extraction, including root volume, length, branching, diameter, and luminance (bright vs. dark roots). Polar metabolites from the roots and leaves were analyzed using gas chromatography–mass spectrometry. Hydric stress caused a significant reduction in g<sub>s</sub> and LWP, which, in turn, led to decreased aerial growth and an accumulation of soluble sugars in the leaves. Root system analysis revealed contrasting trends in root volume, diameter and other related traits. In conclusion, the integration of AI-based root phenotyping with aerial phenotyping and metabolomics has proved to be an effective approach to demonstrate the effects of water deficit in a scarcely investigated species.</p>

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AI-assisted rhizotron phenotyping and metabolomics reveal avocado responses to prolonged water deficit

  • 期刊论文

  • 发表日期：

    2026年4月4日

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  • Clemens P. Beyer,
  • Excequel Ponce,
  • Sebastian Veas,
  • Italo F. Cuneo,
  • Miguel Urrestarazu,
  • Romina Pedreschi,
  • Juan E. Alvaro

摘要

Key message

Prolonged hydric deficit significantly reduces the transpiration and growth of adult avocado trees, while also accumulating soluble sugars in the leaves and reducing root volume and diameter.

Abstract

Climate change and exploitation is expected to drastically reduce water availability in central Chile by placing avocado (Persea americana Mill.) production at severe risk owing to the crop´s high water footprint. Current research on avocado hydric stress remains limited in scope and has typically focused exclusively on aerial plant parts of avocado, with little attention paid to belowground responses. Root phenotyping, defined as the systematic evaluation of root system architecture (RSA), offers a powerful tool for investigating stress adaptation, particularly when combined with aerial phenotyping and metabolomics. In the present study, 28-month-old avocado trees (cv. Hass) grafted onto Mexicola rootstock were grown in 55-L rhizotrons under drip fertigation and subjected to four months of water deficit (“drought stress”). Aerial phenotyping included tree height, trunk area, leaf water potential (LWP) and stomatal conductance (gs). Root phenotyping was conducted using an artificial intelligence-based deep learning convolutional neural network with RootPainter coupled with RhizoVision Explorer for quantitative trait extraction, including root volume, length, branching, diameter, and luminance (bright vs. dark roots). Polar metabolites from the roots and leaves were analyzed using gas chromatography–mass spectrometry. Hydric stress caused a significant reduction in gs and LWP, which, in turn, led to decreased aerial growth and an accumulation of soluble sugars in the leaves. Root system analysis revealed contrasting trends in root volume, diameter and other related traits. In conclusion, the integration of AI-based root phenotyping with aerial phenotyping and metabolomics has proved to be an effective approach to demonstrate the effects of water deficit in a scarcely investigated species.