Detecting nutrient deficiencies in Eucalyptus grandis trees using hyperspectral remote sensing and random forest

Nutrient deficiencies in commercial forest trees often lead to stunted growth and reduced chances of field survival, resulting in a loss of time, productivity, and trees that can become more susceptible to a host of infections. While conventional foliar analytical methods provide accurate results, they are not time and cost-effective in a high productivity environment. This study aims to test the capability of remote sensing to detect macronutrient and micronutrient deficiencies rapidly in juvenile trees. We acquired full-waveform hyperspectral data (350nm-2500nm) from 135 young trees planted in individual pots in a controlled forestry nursery environment. We quantified nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) in young commercially planted forest variety. This study identified the most critical wavebands for detecting nutrient deficiencies using built-in random forest (RF) measures of variable importance. The random forest algorithm's robustness significantly reduced the dataset's noise whilst producing promising results for certain macronutrients such as P and N (0.95 and 0.89, respectively) and micronutrients such as Mn and Cu (0.90 and 0.86, respectively). We identified the red-edge, near-infrared (NIR), visible and short-wave infrared-2 (SWIR-2) regions of the electromagnetic spectrum as the most effective regions for detecting macronutrients and micronutrients in this study. We recommend testing the use of strategic portions of the electromagnetic spectrum for reducing noise and enabling faster computing time, such as portable near-infrared technology.