Detecting nutrient deficiencies in Eucalyptus grandis trees using hyperspectral remote sensing

Leeth Singh

Abstract


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 (350-2500nm) from 135 young trees planted in individual pots in a controlled forestry nursery environment. We tested remote sensing capabilities of detecting nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) which is usually measured in young commercially planted forest varieties when assessing the nutritional status of plant hybrids. The robustness of the random forest algorithm significantly reduced noise in the dataset, 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). This study identified the most critical wavebands for detecting nutrient deficiencies using built-in random forest (RF) measures of variable importance. 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.

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