Cao YiFei, Xu Huanliang, Song Jin, Yang Yao, Hu Xiaohui, Wiyao Korohou Tchalla, Zhai Zhaoyu
College of Engineering, Nanjing Agricultural University, Nanjing, 210032, Jiangsu, China.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
Plant Methods. 2022 May 18;18(1):67. doi: 10.1186/s13007-022-00898-8.
The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil-plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress.
In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods.
The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R, RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively.
This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.
叶绿素含量是反映植物光合作用能力的重要指标，在监测植物整体健康状况方面发挥着重要作用。由于叶绿素含量与土壤-植物分析发展（SPAD）值呈正相关，通过高光谱图像计算植被指数（VIs）来预测SPAD值是可行的，从而评估植物病害的严重程度。然而，目前的指数仅采用了高光谱信息的少数波长，这可能会降低预测准确性。此外，很少有研究探讨植被指数在水稻白叶枯病胁迫下的适用性。
在本研究中，通过计算高光谱曲线（420至950纳米）的光谱分形维数指数（SFDI）来预测SPAD值。进一步分析SPAD值与高光谱信息之间的相关性，以确定对应于不同病害水平的敏感波段。此外，基于所选指数和四种机器学习方法的组合建立了SPAD预测模型。
结果表明，不同病害水平下水稻叶片的SPAD值对不同波长敏感。与当前的植被指数相比，SPAD值与SFDI之间检测到更强的正相关，平均相关系数达到0.8263。对于预测模型，基于支持向量回归和SFDI构建的模型表现最佳，R、RMSE和RE分别达到0.8752、3.7715和7.8614%。
本研究为准确、稳健地预测水稻白叶枯病胁迫下水稻叶片的SPAD值提供了深入见解，光谱分形维数指数对于大面积田间无损监测叶绿素含量具有重要意义。
