The plant bioelectric potential is believed to be a suitable real-time and noninvasive method that can be used to evaluate plant activities, such as the photosynthetic reaction. The amplitude of the bioelectric potential response when plants are illuminated is correlated with the photosynthetic rate. However, practically, the bioelectric potential is affected by various cultivation parameters. This study analyzes the relationship between the bioelectric potential response and the illuminating parameters using a neural network to improve the accuracy of the photosynthetic rate evaluation. The variation of the illuminating colors to the plant affected the relationship between the amplitude of the bioelectric potential response and the photosynthetic rate; therefore, evaluating the photosynthetic rate using the amplitude is difficult. The analysis result shows that the correlation coefficient between the actual measured photosynthetic rate and the estimated photosynthetic rate by the neural network is 0.95. The photosynthetic rate evaluation using the bioelectric potential response is improved and this correlation coefficient is greater than that analyzed by the neural network using only the illuminating parameters. This result indicates that the information on the plant bioelectric potential response contributed to the accurate estimation of the photosynthetic rate.

中文翻译：

---

利用照明信息和神经网络改进植物生物电势评估光合速率

植物生物电位被认为是一种合适的实时和非侵入性方法，可用于评估植物活动，例如光合作用反应。植物被光照时生物电势响应的幅度与光合速率相关。然而，实际上，生物电势受各种培养参数的影响。本研究利用神经网络分析生物电位响应与光照参数之间的关系，以提高光合速率评估的准确性。光照颜色对植物的变化影响生物电位响应幅值与光合速率的关系；因此，使用振幅来评估光合速率是困难的。分析结果表明，实测光合速率与神经网络估计光合速率的相关系数为0.95。使用生物电位响应的光合速率评估得到改进，并且该相关系数大于仅使用照明参数的神经网络分析的相关系数。该结果表明植物生物电势响应的信息有助于准确估计光合速率。使用生物电位响应的光合速率评估得到改进，并且该相关系数大于仅使用照明参数的神经网络分析的相关系数。该结果表明植物生物电势响应的信息有助于准确估计光合速率。使用生物电位响应的光合速率评估得到改进，并且该相关系数大于仅使用照明参数的神经网络分析的相关系数。该结果表明植物生物电势响应的信息有助于准确估计光合速率。