Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens' uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 mu g cm(-2).