Bappa Das, Manohara KK, Gopal R. Mahajan
<span title="2020-03-21">2020</span> <i title="figshare"> Figshare </i> &nbsp;
In the present investigation, spectral signature of leaf samples from 56 different rice genotypes covering both salinity stress tolerant and sensitive were collected at maximum tillering and flowering stage in visible, near-infrared (VNIR) domain for development of non-invasive high-throughput phenotyping techniques for salinity stress monitoring. The spectral reflectance data and K, Na, Ca, Mg, Fe, Mn, Zn and Cu content in leaf samples were analyzed for optimum index identification and partial
more &raquo; ... least square regression (PLSR) model development. The performance of PLSR model was found good for K, Ca, Na, Zn and Cu with r value > 0.70 and d-index more than 0.80 while it was moderate for Mg (r = 0.60 and d-index = 0.72) during calibration. During validation the performance of the developed models was slightly reduced with r value and d-index of > 0.60 and > 0.70, respectively for K, Ca, Na, Zn and Cu. The correlation coefficient (r) and d-index of PLSR model developed for Mg during validation were 0.52 and 0.66, respectively. The results obtained in the present study showed potential of hyperspectral remote sensing for nondestructive phenotyping of salinity stress.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.6084/m9.figshare.12015492.v1</a> <a target="_blank" rel="external noopener" href="">fatcat:ds4obzyytnbfrj3xd5t4lnxwne</a> </span>
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