Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping

Abstract

Hyperspectral reflectance imaging is an emerging method for rapid non-invasive quantitative screening of plant traits. This method is essential for high-throughput phenotyping and hence for accelerated breeding of crop plants as well as for precision agriculture practices. However, extraction of sensible information from reflectance images is hindered by the complexity of plant optical properties, especially when they are measured in the field. We propose using reflectance indices (Plant Senescence Reflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previously developed for remote sensing of vegetation and point-based reflectometers to infer the spatially resolved information on plant development and biochemical composition using ripening apple fruit as the model. Specifically, the proposed approach enables capturing data on distribution of chlorophylls and primary carotenoids as well as secondary carotenoids (both linked with fruit ripening and leaf senescence during plant development) as well as the information on spatial distribution of anthocyanins (known as stress pigments) over the plant surface. We argue that the proposed approach would enrich the phenotype assessments made on the base of reflectance image analysis with valuable information on plant physiological condition, stress acclimation state, and the progression of the plant development.

Development of an Optical System with an Orientation Module to Detect Surface Damage to Potato Tubers

“…One of the most common recognition tasks is the task of identifying objects in an image. If it is known in advance what exactly the object of interest is and its descriptions are set (images from different shooting angles and geometric dimensions, and other parameters of the object are set), then the recognition task is independent and can be reduced to the task of comparing existing descriptions with incoming data, and in cases of coincidence, the object is considered recognized [2]. The definition of the objects of analysis also significantly depends on the technical characteristics of a particular object, such as varietal characteristics, size, the quality of the crop at that time period and the presence or absence of diseases [3][4][5].…”