Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.

Abiotic stress can alter key physiological constituents and functions in green plants. Improving the capacity to monitor this response in a non-destructive manner is of considerable interest, as it would offer a direct means of initiating timely corrective action. Given the vital role that plant pigments play in the photosynthetic process and general plant physiological condition, their accurate estimation would provide a means to monitor plant health and indirectly determine stress response. The aim of this work is to evaluate the response of leaf chlorophyll and carotenoid (C t ) content in wheat (Triticum aestivum L.) to changes in varying application levels of soil salinity and fertilizer applied over a complete growth cycle. The study also seeks to establish and analyze relationships between measurements from a SPAD-502 instrument and the leaf pigments, as extracted at the anthesis stage. A greenhouse pot experiment was conducted in triplicate by employing distinct treatments of both soil salinity and fertilizer dose at three levels. Results showed that higher doses of fertilizer increased the content of leaf pigments across all levels of soil salinity. Likewise, increasing the level of soil salinity significantly increased the chlorophyll and C t content per leaf area at all levels of applied fertilizer. However, as an adaptation process and defense mechanism under salinity stress, leaves were found to be thicker and narrower. Thus, on a per-plant basis, increasing salinity significantly reduced the chlorophyll (Chl t ) and C t produced under each fertilizer treatment. In addition, interaction effects of soil salinity and fertilizer application on the photosynthetic pigment content were found to be significant, as the higher amounts of fertilizer augmented the detrimental effects of salinity. A strong positive (R 2 = 0.93) and statistically significant (p < 0.001) relationship between SPAD-502 values and Chl t and between SPAD-502 values and C t content (R 2 = 0.85) was determined based on a large (n = 277) dataset. We demonstrate that the SPAD-502 readings and plant photosynthetic pigment content per-leaf area are profoundly affected by salinity and nutrient stress, but that the general form of their relationship remains largely unaffected by the stress. As such, a generalized regression model can be used for Chl t and C t estimation, even across a range of salinity and fertilizer gradients.

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