A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging

Abstract

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

Increased Plant Growth with Hematite Nanoparticle Fertilizer Drop and Determining Nanoparticle Uptake in Plants Using Multimodal Approach

“…Electron microscopy is traditionally used to visualize NPs within the plants, but the sample preparation required for this method increases the probability of artifacts in the images. Therefore, we chose hyperspectral imaging (CytoViva), a minimally invasive and enhanced darkfield imaging technique requiring negligible sample preparation to investigate the uptake of hematite NPs in the legumes [29,40]. The enhanced darkfield illumination technology in this method minimized light loss and enabled scatter from the sample to be detected without source illumination interference.…”

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale

“…The literature shows that the use of erosion as a binary image processing technique is efficient. A filter element with a size of 3 × 3 pixels is used to shrink the region of the foreground [ 54 ]. An adverse effect related to the reduction of foreground data is the possibility of losing important information that can be used to enhance the data quality.…”

A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery

“…Afterwards, the extent of each vine within a plot was determined based on the markers' location, spatial resolution (~1 cm/pixel), and vines spacing (1.83 m). Lastly, pixels representing the canopy were segmented from the background using a binary mask obtained from applying empirical thresholds on the excess green index (EGI) and NDVI (i.e., multiplying EGI and NDVI binary masks) [29] ( Figure 1B). The canopy pixels located in the shaded area were removed during the segmentation process.…”

“…The emergence of advanced machine learning techniques, along with high-performance computational power, has provided new opportunities to translate image-based datasets into novel insights. In agriculture, machine learning and deep learning have been recently implemented to analyze images captured for various applications, such as biotic stress detection [26,27], abiotic stress detection [28,29], nitrogen estimation [30,31], spectral features selection for high-throughput phenotyping [32], weed detection [33,34] and yield prediction [17,35].…”