Enhancing Kiwi Bacterial Canker Leaf Assessment: Integrating Hyperspectral-based Vegetation Indexes in Predictive Modeling

The potential of hyperspectral UV–VIS–NIR reflectance for in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in-vivo, in-situ, with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal, for 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for non-destructive discrimination of bacterial canker on kiwi leaves.