Hyperspectral image-based leaf-level spatial and spectral feature mining for phosphorus deficiency symptom differentiation in corn plants at early vegetative stage

Abstract

Phosphorus (P) is a vital macronutrient necessary for synthesizing essential plant biomolecules. Accurate identification of plant P deficiency symptoms is critical for effective crop management and optimizing crop yield. Hyperspectral sensing provides a real-time, non-destructive avenue for assessing crop nutrient status, while its performance largely depends on the representativeness of the extracted features. In this study, a handheld proximal transmittance hyperspectral imager, LeafSpec, was utilized to collect leaf-level hyperspectral images at corn V6 vegetative stage. A novel feature mining algorithm was proposed to extract and combine the spatial and spectral features in visible and near-infrared range, enabling effective differentiation of P deficiency. The correlation coefficient between the P content and the selected spatial-spectral features reached 0.77. Compared with spectral indices, the combined spatial-spectral feature showed a more significant differences among corn plants under different P treatments, especially between the medium and sufficient P levels. Feature visualization heatmaps, highlighting leaf venation variations with spatial-spectral calculations, provided direct evidences of effectiveness. This study shows the potential of integrating handheld proximal transmittance hyperspectral imaging with feature mining algorithm for early-stage differentiation of P levels in corn plants.

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