Assessing the nitrogen status of almond trees by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates

Tarin Paz-Kagan, Ze'ev Schmilovitch, Uri Yermiyahu, Tal Rapaport, Or Sperling

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Precise nitrogen (N) fertilization requires new indices of plants’ nutritional status. Non-structural carbohydrates (NSC) are the energetic currency of plants and can, thus, serve as a physiological indicator for their condition. Nevertheless, only a few records exist about NSC composition and allocation in crops, and their relationship with N uptake and the current methods to detect NSC compositions in plants are cumbersome and expensive, which limits their use. The current work aimed to associate the nutritional status of almond trees with the carbohydrate compositions in their roots, branches, and leaves by a high-throughput technique. We found that low N availability forces trees to allocate carbohydrates to their roots. High N availability, on the other hand, promoted above-ground vegetative growth, and minimized carbohydrate storage in the leaves. These observations implied that carbohydrate distribution could, indeed, serve as an indication of the nutritional status of the trees. To measure NSC content efficiently, we attempted to quantify soluble carbohydrates and starch in dried and powdered tissues by visible-to-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) reflectance spectroscopy, which is an inexpensive, safe, and non-destructive technique. We applied several multivariate statistical models based on the spectral datasets, including partial least squares-regression (PLS-R) and discriminant analysis (PLS-DA) as supervised registration and classification models. PLS-DA results of the N gradient in the roots and leaves showed an overall accuracy of 94% and 98%, respectively. PLS-R model performances of soluble carbohydrates and starch improved, in terms of the coefficient of determination (R2), if the leaf and root samples were integrated. Moreover, we found that the SWIR spectral region (1100–2500 nm) had unique reflectance features that revealed the carbohydrate composition and starch concentrations in the different plant tissues. The analyses also clustered the reflectance by tree part (root, branch, or leaf tissues) and N availability, forming a holistic model that can identify the nutritional status of trees. Conclusively, it is suggested that reflectance spectroscopy at the SWIR spectral region could guide precise fertilization by high-throughput identification of plants’ seasonal metabolism.

Original languageEnglish
Article number105755
JournalComputers and Electronics in Agriculture
Volume178
DOIs
StatePublished - 1 Nov 2020
Externally publishedYes

Keywords

  • Multivariate statistics
  • Non-structural carbohydrates
  • Nutritional status
  • Partial least squares
  • Precision agriculture
  • Soluble carbohydrates
  • Starch

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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