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Intelligent Monitoring and Precision Management of Tea Gardens Based on Sun-Induced Chlorophyll Fluorescence (SIF)

日光诱导叶绿素荧光（厂滨贵）作为一种新兴技术，在茶叶的叶绿素含量、氮监测和胁迫监测等方面具有潜在的应用价值。通过监测这些关键指标，可以更好地了解茶叶的生长状况、品质和对环境胁迫的响应，从而为茶叶的精准管理提供支持。

Sun-induced chlorophyll fluorescence (SIF) is an emerging technology with potential applications in monitoring chlorophyll content, nitrogen status, and stress responses in tea plants. By tracking these key indicators, it is possible to gain deeper insights into the growth status, quality, and environmental adaptability of tea plants, thereby supporting precision management in tea production.

「茶叶叶绿素含量 / Chlorophyll Content in Tea Leaves」

日光诱导叶绿素荧光技术，可实现对叶绿素含量的快速无损监测，这对茶树生长管理具有重要意义。

叶绿素是茶叶光合作用的关键色素，其含量直接影响光合效率，是其健康生长的基础，与并茶树长势密切相关。

研究表明，适宜的遮阴处理可通过提高叶绿素含量改善茶叶品质，而厂滨贵技术能精准量化这种品质关联——通过追踪荧光信号变化，可确定适合的遮阴时长与强度，确保叶绿素含量维持在利于氨基酸、可溶性糖积累的合理范围。

SIF technology enables rapid, non-destructive monitoring of chlorophyll content, which is highly significant for the growth management of tea plants.

Chlorophyll is a key pigment in photosynthesis, and its content directly affects photosynthetic efficiency—fundamental to healthy growth—and is closely related to the vigor of tea plants.

Studies have shown that appropriate shading can improve tea quality by increasing chlorophyll content. SIF technology can accurately quantify this relationship: by tracking changes in fluorescence signals, the optimal shading duration and intensity can be determined to ensure chlorophyll levels remain within a range conducive to the accumulation of amino acids and soluble sugars.

不同种植条件影响茶叶的叶绿素含量，并影响其颜色、风味和香气。

Different planting conditions affect the chlorophyll content in tea leaves, thereby influencing their color, flavor, and aroma.

「氮监测 / Nitrogen Monitoring 」

氮是茶叶生长必需的营养元素，对叶绿素合成和光合作用至关重要。传统的氮肥管理依赖于经验和土壤测试，难以实现精准施肥。厂滨贵技术为茶叶的氮监测提供了一种新的途径。

研究表明，厂滨贵信号与植物的氮营养状况密切相关。氮素充足时，植物光合作用旺盛，厂滨贵信号较强；氮素不足时，光合作用受限，厂滨贵信号减弱。厂滨贵技术也有望于应用在茶叶种植上，通过分析厂滨贵信号，判断茶叶的氮营养状况，从而指导施肥管理，提高茶叶产量和品质。

通过厂滨贵信号反映的氮营养状况，能够精准调整氮肥用量，避免过量施肥造成的环境污染和资源浪费；同时，基于氮素需求的精准施肥可充分满足茶树生长所需，有效促进光合作用，进而提高茶叶产量；此外，适宜的氮营养还有助于提升叶绿素和氨基酸含量，显着改善茶叶的整体品质。

Nitrogen is an essential nutrient for tea growth, playing a critical role in chlorophyll synthesis and photosynthesis. Traditional nitrogen management relies on experience and soil testing, making precise fertilization challenging. SIF technology offers a new approach to nitrogen monitoring in tea  farming.

Research indicates that SIF signals are closely correlated with the nitrogen status of plants. Under sufficient nitrogen supply, photosynthesis is vigorous, and SIF signals are strong; under nitrogen deficiency, photosynthesis is inhibited, and SIF signals weaken. SIF technology shows promise for application in tea production—by analyzing SIF signals, the nitrogen status of tea plants can be assessed, guiding fertilization management to improve yield and quality.

Using SIF-reflected nitrogen status, fertilizer application can be precisely adjusted to avoid environmental pollution and resource waste caused by over-fertilization. Meanwhile, precision fertilization based on nitrogen demand fully supports tea plant growth, effectively enhances photosynthesis, and thereby increases yield. Furthermore, optimal nitrogen nutrition helps elevate chlorophyll and amino acid content, significantly improving overall tea quality.

「茶叶胁迫监测 / Stress Monitoring in Tea Plants 」

茶叶在生长过程中会受到多种胁迫，如干旱、病虫害等。这些胁迫会影响茶叶的光合作用和生长，降低产量和品质。厂滨贵技术可以用于茶叶的胁迫监测。

研究表明，厂滨贵对植物的生理变化敏感，可用于检测棉花黄萎病等病害。因此，厂滨贵也有望应用于茶叶病虫害的早期诊断。

水分胁迫也会影响茶叶的生长。通过分析厂滨贵对不同程度水分胁迫的响应，可以实现对茶叶水分状况的监测。

Tea plants are subject to various stresses during growth, such as drought, pests, and diseases. These stressors can negatively impact photosynthesis and growth, reducing yield and quality. SIF technology can be applied to monitor stress in tea plants.

Studies demonstrate that SIF is sensitive to physiological changes in plants and can be used to detect diseases such as cotton Verticillium wilt. Therefore, SIF also holds potential for early diagnosis of pests and diseases in tea plants.

Water stress similarly affects tea plant growth. By analyzing the response of SIF to varying degrees of water stress, it is possible to monitor the water status of tea plants.

茶芽出现枯焦 / Tea buds appear scorched.

在实际应用中，厂滨贵监测系统正变得越来越智能。

以爱博能开发的日光诱导叶绿素荧光(厂滨贵)监测系统为例，该系统内置先进算法，能够直接输出叶绿素荧光产额和光合作用速率等关键参数，用户无需再进行复杂的数据分析处理。系统提供塔台在线式长期监测与无人机载机动巡测两种型号，既可满足茶园固定点的连续观测需求，也能适应大范围、多茶区的快速评估场景，为不同规模的茶园提供定制化解决方案。

日光诱导叶绿素荧光技术为茶叶生产提供了实时、无损且高效的监测新方式，覆盖生长、营养与胁迫等多维度管理需求。随着该技术不断成熟与应用深化，它将在推动茶叶精准种植、提升茶叶品质与产业可持续发展方面发挥越来越重要的作用。

In practical applications, SIF monitoring systems are becoming increasingly intelligent.

For example, the SIF monitoring system developed by ExponentSci contains advanced algorithms that directly output key parameters such as chlorophyll fluorescence yield and photosynthetic rate, eliminating the need for users to perform complex data processing. The system offers two models: a tower-based online version for long-term monitoring and a drone-mounted mobile version for rapid large-scale assessments across multiple tea regions. This provides customized solutions for tea gardens of different scales.

SIF technology offers a real-time, non-destructive, and efficient monitoring method for tea production, addressing multidimensional management needs including growth, nutrition, and stress. As the technology continues to mature and find broader applications, it will play an increasingly important role in promoting precision tea  farming, improving tea quality, and supporting sustainable industry development.

案例来源 / Sources：

1. Ma, X., Liu, J., Li, H., Wang, W., Liu, L., Wang, P., Hu, J., Zhang, X., & Qu, F. (2024). Greenhouse covering **** promotes chlorophyll accumulation of tea plant (Camellia sinensis) by activating relevant gene expression and enzyme activity. BMC Plant Biology, 24(1).

2. XIANG, F., LI, W., LIU, H., ZHOU, L., & JIANG, C. (2018). Characteristics of Photosynthetic and Chlorophyll Fluorescence of Tea Varieties under Different Nitrogen Application Levels. In Acta Botanica Boreali-Occidentalia Sinica.

3. Wang, C., Wang, Z., Chen, L., Liu, W., Wang, X., Cao, Z., Zhao, J., Zou, M., Li, H., Yuan, W., & Wang, B. (2025). Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging. Plants, 14(13), 1965.