A Hyperspectral Image-Based Method for Estimating Water and Chlorophyll Contents in Maize Leaves under Drought Stress

ObjectivesChlorophyll content and water content are key physiological indicators of crop growth, and their non-destructive detection is a key technology to realize the monitoring of crop growth status such as drought stress. This study took maize as an object to develop a hyperspectral-based approach for the rapid and non-destructive acquisition of the leaf chlorophyll content and water content for drought stress assessment.MethodsDrought treatment experiments were carried out in a greenhouse of the College of Agriculture, Guangxi University. Maize plants were subjected to drought stress treatment at the seedling stage (four leaves). Four drought treatments were set up for normal water treatment [CK], mild drought [W1], moderate drought [W2], and severe drought [W3], respectively. Leaf samples were collected at the 3rd, 6th, and 9th days after drought treatments, and 288 leaf samples were collected in total, with the corresponding chlorophyll content and water content measured in a standard laboratory protocol. A pair of push-broom hyperspectral cameras were used to collect images of the 288 seedling maize leaf samples, and image processing techniques were used to extract the mean spectra of the leaf lamina part. The algorithm flow framework of "pre-processing - feature extraction - machine learning inversion" was adopted for processing the extracted spectral data. The effects of different pre-processing methods, feature wavelength extraction methods and machine learning regression models were analyzed systematically on the prediction performance of chlorophyll content and water content, respectively. Accordingly, the optimal chlorophyll content and water content inversion models were constructed. Firstly, 70% of the spectral data was randomly sampled and used as the training dataset for training the inversion model, whereas the remaining 30% was used as the testing dataset to evaluate the performance of the inversion model. Subsequently, the effects of different spectral pre-processing methods on the prediction performance of chlorophyll content and water content were compared. Different feature wavelengths were extracted from the optimal pre-processed spectra using different algorithms, then their capabilities in preserve the information useful for the inversion of leaf chlorophyll content and water content were compared. Finally, the performances of different machine learning regression model were compared, and the optimal inversion model was constructed and used to visualize the chlorophyll content and water content. Additionally, the construction of vegetation coefficients were explored for the inversion of chlorophyll content and water content and evaluated their inversion ability. The performance evaluation indexes used include determination coefficient and root mean squared error (RMSE).Results and DiscussionsWith the aggravation of stress, the reflectivity of leaves in the wavelength range of 400~1700 nm gradually increased with the degree of drought stress. For the inversion of leaf chlorophyll content and water content, combining stepwise regression (SR) feature extraction with Stacking regression could obtain an optimal performance for chlorophyll content prediction, with an R2 of 0.878 and an RMSE of 0.317 mg/g. Compared with the full-band stacking model, SR-Stacking not only improved R2 by 2.9%, reduced RMSE by 0.0356mg/g, but also reduced the number of model input variables from 1301 to 9. Combining the successive projection algorithm (SPA) feature extraction with Stacking regression could obtain the optimal performance for water content prediction, with an R2 of 0.859 and RMSE of 3.75%. Compared with the full-band stacking model, SPA-Stacking not only increased R2 by 0.2%, reduced RMSE by 0.03%, but also reduced the number of model input variables from 1301 to 16. As the newly constructed vegetation coefficients, normalized difference vegetation index(NDVI) [(R410-R559)/(R410+R559)] and ratio index (RI) (R400/R1171) had the highest accuracy and were significantly higher than the traditional vegetation coefficients for chlorophyll content and water content inversion, respectively. Their R2 were 0.803 and 0.827, and their RMSE were 0.403 mg/g and 3.28%, respectively. The chlorophyll content and water content of leaves were visualized. The results showed that the physiological parameters of leaves could be visualized and the differences of physiological parameters in different regions of the same leaves can be found more intuitively and in detail.ConclusionsThe inversion models and vegetation indices constructed based on hyperspectral information can achieve accurate and non-destructive measurement of chlorophyll content and water content in maize leaves. This study can provide a theoretical basis and technical support for real-time monitoring of corn growth status. Through the leaf spectral information, according to the optimal model, the water content and chlorophyll content of each pixel of the hyperspectral image can be predicted, and the distribution of water content and chlorophyll content can be intuitively displayed by color. Because the field environment is more complex, transfer learning will be carried out in future work to improve its generalization ability in different environments subsequently and strive to develop an online monitoring system for field drought and nutrient stress.