Development of multi-sensing technologies for high-throughput morphological, physiological, and biochemical phenotyping of drought-stressed watermelon plants

IF 5.7 2区生物学 Q1 PLANT SCIENCES

Plant Physiology and Biochemistry Pub Date : 2025-10-01 DOI:10.1016/j.plaphy.2025.110577

Mohammad Akbar Faqeerzada , Eunsoo Park , Jinsu Lim , Kihyun Kim , Ramaraj Sathasivam , Sang Un Park , Hangi Kim , Byoung-Kwan Cho

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引用次数: 0

Abstract

High-throughput plant phenotyping (HTPP) technologies are rapidly transforming plant science by enabling real-time, non-invasive, and large-scale monitoring of complex morphological, physiological, and biochemical traits. However, existing platforms often lack integration across sensing modalities and analytical depth necessary for early and comprehensive phenotypic trait analysis. In this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants. RGB imaging facilitated detailed morphological analysis by extracting color-based traits, quantifying plant height and canopy area, and accurately distinguishing growth stages. SWIR hyperspectral imaging (HSI) enabled non-invasive biochemical assessment by detecting drought-responsive compounds, such as flavonoids, phenolics, and antioxidant activities, while also supporting the classification of stress severity. This spectral profiling revealed key biochemical alterations triggered by water deficit. MSFI liquid crystal tunable filter (LCTF-based) measured chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (t-Chl) levels, providing critical insights into photosynthetic performance under drought stress. Thermal imaging further enhanced drought assessment by capturing canopy temperature variations, which were used to derive thermal indices for indirect estimation of soil volumetric water content (SVWC). By integrating complementary imaging modalities, the proposed system captured comprehensive phenotypic responses with high predictive accuracy for early detection of drought stress and assessment of plant health. Advanced machine learning (ML) and deep learning (DL) models further enhanced trait extraction and classification, enabling robust analysis of complex, high-dimensional data. This automated, multimodal platform offers scalable, non-invasive crop monitoring, providing precise insights to support drought resilience and precision agriculture.

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干旱胁迫西瓜植株形态、生理和生化表型高通量多传感技术的发展

高通量植物表型（HTPP）技术通过实时、无创和大规模监测复杂的形态、生理和生化性状，正在迅速改变植物科学。然而，现有的平台往往缺乏跨传感模式和分析深度的整合，这是早期和全面的表型性状分析所必需的。在这项研究中，我们开发了一个全自动、多模态HTPP系统，结合RGB、短波红外（SWIR）高光谱、多光谱荧光成像（MSFI）和热成像来表征干旱胁迫西瓜（Citrullus lanatus）植物。RGB成像通过提取基于颜色的性状，量化植物高度和冠层面积，准确区分生长阶段，为详细的形态分析提供了便利。SWIR高光谱成像（HSI）通过检测干旱响应化合物（如类黄酮、酚类物质和抗氧化活性）实现了无创生化评估，同时也支持胁迫严重程度的分类。这一光谱分析揭示了缺水引发的关键生化变化。MSFI液晶可调谐滤波器（LCTF-based）测量了叶绿素a （Chl-a）、叶绿素b （Chl-b）和总叶绿素（t-Chl）水平，为干旱胁迫下的光合性能提供了重要的信息。热成像通过捕获冠层温度变化进一步增强了干旱评估，利用冠层温度变化获得间接估算土壤体积含水量（SVWC）的热指数。通过整合互补成像模式，该系统捕获了全面的表型反应，具有较高的预测准确性，可用于早期检测干旱胁迫和评估植物健康。先进的机器学习（ML）和深度学习（DL）模型进一步增强了特征提取和分类，实现了对复杂、高维数据的鲁棒分析。这种自动化的多模式平台提供可扩展的非侵入性作物监测，提供精确的见解，以支持抗旱能力和精准农业。

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来源期刊

Plant Physiology and Biochemistry 生物-植物科学

CiteScore

11.10

自引率

3.10%

发文量

410

审稿时长

33 days

期刊介绍： Plant Physiology and Biochemistry publishes original theoretical, experimental and technical contributions in the various fields of plant physiology (biochemistry, physiology, structure, genetics, plant-microbe interactions, etc.) at diverse levels of integration (molecular, subcellular, cellular, organ, whole plant, environmental). Opinions expressed in the journal are the sole responsibility of the authors and publication does not imply the editors'' agreement. Manuscripts describing molecular-genetic and/or gene expression data that are not integrated with biochemical analysis and/or actual measurements of plant physiological processes are not suitable for PPB. Also "Omics" studies (transcriptomics, proteomics, metabolomics, etc.) reporting descriptive analysis without an element of functional validation assays, will not be considered. Similarly, applied agronomic or phytochemical studies that generate no new, fundamental insights in plant physiological and/or biochemical processes are not suitable for publication in PPB. Plant Physiology and Biochemistry publishes several types of articles: Reviews, Papers and Short Papers. Articles for Reviews are either invited by the editor or proposed by the authors for the editor''s prior agreement. Reviews should not exceed 40 typewritten pages and Short Papers no more than approximately 8 typewritten pages. The fundamental character of Plant Physiology and Biochemistry remains that of a journal for original results.