Abstract Plants’ dynamic eco-physiological responses are vital to their productivity in continuously fluctuating conditions, such as those in agricultural fields. However, it is currently still very difficult to capture these responses at the field scale for phenotyping purposes. Advanced hyperspectral imaging tools are increasingly used in phenotyping, and have been applied to detect changes in plants in response to a specific treatment, phenological state or monitor its growth and development. Phenotyping has to evolve towards capturing dynamic behaviour under more subtle fluctuations in environmental conditions, without the presence of clear treatments or stresses. Therefore, we investigated the potential of hyperspectral imaging to capture dynamic behaviour of plants in stress-free conditions at a temporal resolution of seconds. Two growth chamber experiments were set up, in which strawberry plants and four different background materials, serving as controls, were monitored by a snapshot hyperspectral camera in variable conditions of light, temperature and relative humidity. The sampling period was set to three seconds, triggering image acquisition and gas exchange measurements. Different background materials were used to assess the influence of the environment and the camera in both experiments. To separate the plant and background data, static masks were determined. Two datasets were created, which encompass both experiments. One dataset was constructed after averaging over the entire mask to acquire one value per spectral band. These values were then used to calculate a set of vegetation indices. The other dataset used spatial subsampling to retain spatial information. From both datasets, linear models were constructed using ridge regression, which estimated the measured eco-physiological and environmental data. Leaf temperature and vapour pressure deficit based on leaf temperature are the two main eco-physiological characteristics that could be predicted successfully. Stomatal conductance, photosynthesis and transpiration rate show less promising results. We suspect that limited variation, and low spectral resolution and range are the main causes of the inability of the models to extract meaningful predictions. Furthermore, the models that were only trained on background data also showed good predictive performance. This is probably because the main drivers for good performing eco-physiological variables are temperature and incident light intensity. Environmental characteristics that have good performance are photosynthetically active radiation and air temperature. Current hyperspectral sensing technologies are not yet able to uncover most plant dynamic eco-physiological responses when plants are cultivated in stress-free conditions.

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快照高光谱相机在无压力条件下监测植物响应动态的局限性

摘要 植物的动态生态生理反应对其在不断波动的条件下的生产力至关重要，例如在农业领域。然而，目前为了表型分析的目的，在田间尺度上捕捉这些反应仍然非常困难。先进的高光谱成像工具越来越多地用于表型分析，并已应用于检测植物响应特定处理、物候状态的变化或监测其生长和发育。表型分析必须朝着在环境条件更微妙的波动下捕捉动态行为的方向发展，而没有明确的治疗或压力。因此，我们研究了高光谱成像在无压力条件下以秒的时间分辨率捕捉植物动态行为的潜力。设置了两个生长室实验，其中草莓植物和四种不同的背景材料作为对照，在光照、温度和相对湿度的可变条件下通过快照高光谱相机进行监测。采样周期设置为三秒，触发图像采集和气体交换测量。在这两个实验中，使用不同的背景材料来评估环境和相机的影响。为了分离植物和背景数据，确定了静态掩膜。创建了两个数据集，其中包含两个实验。在对整个掩模求平均以获取每个光谱带的一个值后，构建了一个数据集。然后将这些值用于计算一组植被指数。另一个数据集使用空间子采样来保留空间信息。从这两个数据集，线性模型是使用岭回归构建的，该模型估计了测量的生态生理和环境数据。叶温和基于叶温的蒸气压亏缺是可以成功预测的两个主要生态生理特征。气孔导度、光合作用和蒸腾速率显示出不太有希望的结果。我们怀疑有限的变化、低光谱分辨率和范围是模型无法提取有意义的预测的主要原因。此外，仅在背景数据上训练的模型也表现出良好的预测性能。这可能是因为良好表现的生态生理变量的主要驱动因素是温度和入射光强度。具有良好性能的环境特征是光合有效辐射和气温。当前的高光谱传感技术还不能揭示在无压力条件下种植植物时的大多数植物动态生态生理反应。