The monitoring of the crop water status is fundamental to be assessed in order to implement precision irrigation criteria in the field. In this study, a combined approach based on the use of multispectral imagery, acquired by an unmanned aerial vehicle (UAV) system, and statistical models was applied for determining the water status of a citrus orchard during different phenological stages, with respect to traditional stem water potential (SWP) measurements. Specifically, the suitability of individual spectral bands and vegetation indices was firstly evaluated, followed by the implementation of statistical methods (i.e., stepwise linear regression models and principal component regression) using the UAV information as explanatory variables. The results showed a weak correlation between the spectral bands and the SWP during the fruit rapid growth stage. A stronger relationship was observed instead when the vegetation indices were used, during the same phenological stage, but with low Pearson coefficients of correlation, varying from -0.31 to -0.46. The SWP estimated by the statistical methods resulted more reliable, with average mean absolute error, root mean square error and percent bias values ranging from 0.17 to 0.19 MPa, from 0.23 to 0.24 MPa and from -0.38 to -0.59%, respectively. In conclusion, the proposed approach provides a useful tool for monitoring the spatial variability of crop water status aiming at supporting the adoption of precision irrigation strategies

Appraising the stem water potential of citrus orchards from UAV-based multispectral imagery

Giuseppe Longo-Minnolo;Simona Consoli;Daniela Vanella;Serena Guarrera;Giuseppe Manetto;Emanuele Cerruto
2023-01-01

Abstract

The monitoring of the crop water status is fundamental to be assessed in order to implement precision irrigation criteria in the field. In this study, a combined approach based on the use of multispectral imagery, acquired by an unmanned aerial vehicle (UAV) system, and statistical models was applied for determining the water status of a citrus orchard during different phenological stages, with respect to traditional stem water potential (SWP) measurements. Specifically, the suitability of individual spectral bands and vegetation indices was firstly evaluated, followed by the implementation of statistical methods (i.e., stepwise linear regression models and principal component regression) using the UAV information as explanatory variables. The results showed a weak correlation between the spectral bands and the SWP during the fruit rapid growth stage. A stronger relationship was observed instead when the vegetation indices were used, during the same phenological stage, but with low Pearson coefficients of correlation, varying from -0.31 to -0.46. The SWP estimated by the statistical methods resulted more reliable, with average mean absolute error, root mean square error and percent bias values ranging from 0.17 to 0.19 MPa, from 0.23 to 0.24 MPa and from -0.38 to -0.59%, respectively. In conclusion, the proposed approach provides a useful tool for monitoring the spatial variability of crop water status aiming at supporting the adoption of precision irrigation strategies
2023
979-8-3503-1272-0
Crop water status
Precision irrigation
Proximal sensing
Vegetation indices
Spectral bands
File in questo prodotto:
File Dimensione Formato  
Longo-Minnolo et al 2023.pdf

solo gestori archivio

Descrizione: articolo
Tipologia: Versione Editoriale (PDF)
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 653.02 kB
Formato Adobe PDF
653.02 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/602370
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact