Perennial Crop Type Discrimination with AVIRIS-NG Hyperspectral Imagery and Machine Learning

Accurate maps of perennial crop types support agricultural monitoring, water-use accounting, and strategic decisions on climate adaptation. Hyperspectral imagery offers the fine spectral detail needed to separate spectrally similar plant species, but its high dimensionality complicates classification. This study assessed the discrimination of spectrally similar perennial crop types using AVIRIS-NG imagery and random forest classification. Three perennial crop type classification schemes (with 17, nine, and six classes, respectively) were targeted. Classification models were trained on labelled crop type samples collected in the Western Cape Province of South Africa. Feature selection (recursive feature elimination and Boruta) and feature extraction (principal component analysis and the minimum noise fraction transform) were used to reduce image dimensionality. Feature extraction improved accuracy over the spectral baseline across all classification schemes, with the minimum noise fraction transform consistently outperforming principal component analysis, including in the 17-class scheme, and feature extraction alone yielding the highest accuracies. Overall accuracy increased with increased aggregation, from approximately 66% in the 17-class scheme to 75% in the six-class scheme. Spectrally distinct crops such as cherry and macadamia were classified reliably, whereas structurally similar tree crops (e.g., lemon and lime, peach and plum) remained poorly classified. The findings suggest that hyperspectral data holds much potential for differentiating between spectrally similar perennial crop types but that classification schemes must be carefully designed to reduce misclassifications.