Estimation of chlorophyll, macronutrients and water content in maize from hyperspectral data using machine learning and explainable artificial intelligence techniques

We used the secondary hyperspectral data set (leaf reflectance of maize) taken in the spectral range from 350 to 2500 nm in a field (under low and high nitrogen conditions) and glasshouse experiment. Water, chlorophyll, nitrogen (N), phosphorus (P) and potassium (K) contents of maize leaves taken from these experiments were measured using the standard methods. Six machine learning regression algorithms (Random Forest, Support Vector Regression, k-Nearest Neighbours, Multilayer Perceptron, Gradient Boosting Regression and Partial Least Square Regression) were used for development of the models to predict these parameters from leaf reflectance data. Each plant parameter was estimated with a different machine learning algorithm. Explainable artificial intelligence methods were used to identify the optimum wavelengths for each parameter. The wavelengths in the short-wave infrared (SWIR) region were found optimum for estimating the water content across the three N regimes, and the red-edge band for chlorophyll. The optimum wavelengths for estimating N content in leaves were in the green spectral region under low N status, near-infrared (NIR) and SWIR regions under greenhouse and SWIR region under high N status. The important wavelengths for estimating P in maize leaves were 1088, 1262 and 1263 nm under low N status and the SWIR range under high N status and greenhouse conditions. The SWIR band was useful for estimating the K content in leaves.