Pipeline for imaging, extraction, pre-processing, and processing of time-series hyperspectral data for discriminating drought stress origin in tomatoes

Abstract

Crop infestation with root-knot nematodes (RKN) and water deficiency lead to similar visible symptoms in the plant canopy. Identification of biotic or abiotic stress origin is therefore a problem, and currently the only reliable methods for determination of RKN infestation are invasive and applicable only for point-searches. In this study the applicability of hyperspectral remote sensing for early identification of drought stress and RKN infestations in tomato plants was tested. A four-stage image and data management pipeline was established: (1) image acquisition, (2) data extraction, (3) pre-processing, and (4) processing. • This pipeline reduces atmospheric impacts, facilitates data extraction (by using specially designed spectral libraries and supervised classification procedures), diminishes the impact of viewing geometry, and emphasized small spectral variations not apparent in the raw data. • By combining partial least squares – discriminant analysis and support vector machines with time series analysis, we achieved up to 100% classification success when determining watering regime and infestation, and their severity. • This pipeline could be at least partially automated, thus facilitating high throughput identification of stress origin in plants. Furthermore, the same pipeline could be applied to hyperspectral phenotyping procedures, which are gaining importance in breeding programs. Method name: Hyperspectral image processing, Keywords: Hyperspectral imaging, Remote sensing, PLS-DA, PLS-SVM, Drought stress, Root-knot nematode, Meloidogyne, Tomato, Solanum lycopersicum, Detection, Stress, Biotic, Abiotic