Pii Youry, Orzes Guido, Mazzetto Fabrizio, Sambo Paolo, Cesco Stefano
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Bolzano, Italy.
Faculty of Engineering, Free University of Bolzano, Bolzano, Italy.
Front Plant Sci. 2024 Nov 4;15:1459670. doi: 10.3389/fpls.2024.1459670. eCollection 2024.
Modern viticulture faces significant challenges including climate change and increasing crop diseases, necessitating sustainable solutions to reduce fungicide use and mitigate soil health risks, particularly from copper accumulation. Advances in plant phenomics are essential for evaluating and tracking phenotypic traits under environmental stress, aiding in selecting resilient vine varieties. However, current methods are limited, hindering effective integration with genomic data for breeding purposes. Remote sensing technologies provide efficient, non-destructive methods for measuring biophysical and biochemical traits of plants, offering detailed insights into their physiological and nutritional state, surpassing traditional methods. Smart phenotyping is essential for selecting crop varieties with desired traits, such as pathogen-resilient vine varieties, tolerant to altered soil fertility including copper toxicity. Identifying plants with typical copper toxicity symptoms under high soil copper levels is straightforward, but it becomes complex with supra-optimal, already toxic, copper levels common in vineyard soils. This can induce multiple stress responses and interferes with nutrient acquisition, leading to ambiguous visual symptoms. Characterizing resilience to copper toxicity in vine plants smart phenotyping is feasible by relating smart data with physiological assessments, supported by trained professionals who can identify primary stressors. However, complexities increase with more data sources and uncertainties in symptom interpretations. This suggests that artificial intelligence could be valuable in enhancing decision support in viticulture. While smart technologies, powered by artificial intelligence, provide significant benefits in evaluating traits and response times, the uncertainties in interpreting complex symptoms (e.g., copper toxicity) still highlight the need for human oversight in making final decisions.
现代葡萄栽培面临着重大挑战，包括气候变化和作物病害增加，因此需要可持续的解决方案来减少杀菌剂的使用并降低土壤健康风险，尤其是铜积累带来的风险。植物表型组学的进展对于评估和跟踪环境胁迫下的表型性状至关重要，有助于选择具有抗逆性的葡萄品种。然而，目前的方法存在局限性，阻碍了其与基因组数据有效整合用于育种目的。遥感技术提供了高效、无损的方法来测量植物的生物物理和生化特性，能深入了解其生理和营养状况，超越了传统方法。智能表型分析对于选择具有所需性状的作物品种至关重要，例如对病原体具有抗性、能耐受包括铜毒性在内的土壤肥力变化的葡萄品种。在高土壤铜水平下识别具有典型铜毒性症状的植物很简单，但在葡萄园土壤中常见的超最佳、已具毒性的铜水平情况下就变得复杂了。这会引发多种应激反应并干扰养分获取，导致视觉症状模糊不清。通过将智能数据与生理评估相关联，由经过培训的专业人员支持来识别主要应激源，对葡萄植株铜毒性抗性进行智能表型分析是可行的。然而，随着数据源增多和症状解释的不确定性增加，复杂性也随之上升。这表明人工智能在增强葡萄栽培决策支持方面可能具有价值。虽然由人工智能驱动的智能技术在评估性状和响应时间方面带来了显著益处，但在解释复杂症状（如铜毒性）时的不确定性仍然凸显了在做出最终决策时需要人工监督。
