Abstract
The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments were assayed, a control treatment and two regulated deficit irrigation treatments. Four airborne flights were carried out during two consecutive seasons; to compare the results of the remote sensing VI, the conventional and continuous water status indicators commonly used to manage sweet cherry tree irrigation were measured, including midday stem water potential (Ψs) and maximum daily shrinkage (MDS). Simple regression between individual VIs and Ψs or MDS found stronger relationships in postharvest than in preharvest. Thus, the normalized difference vegetation index (NDVI), resulted in the strongest relationship with Ψs (r2 = 0.67) and MDS (r2 = 0.45), followed by the normalized difference red edge (NDRE). The sensitivity analysis identified the optimal soil adjusted vegetation index (OSAVI) as the VI with the highest coefficient of variation in postharvest and the difference vegetation index (DVI) in preharvest. A new index is proposed, the transformed red range vegetation index (TRRVI), which was the only VI able to statistically identify a slight water deficit applied in preharvest. The combination of the VIs studied was used in two machine learning models, decision tree and artificial neural networks, to estimate the extra labor needed for harvesting and the sweet cherry yield.
Highlights
Precision agriculture cannot be conceived without the recent evolution of technology
normalized difference red edge (NDRE) is used in those cases with dense vegetation when normalized difference vegetation index (NDVI) is saturated, since it is computed from the red edge band and it is able to measure deeper into the canopy [9]
Our results showed that control treatment (CTL) trees had higher NDVI values than regulated deficit irrigation (RDI) trees as water-stress reduces the capacity of plants to absorb the red energy and reflect NIR
Summary
Precision agriculture cannot be conceived without the recent evolution of technology Technological tools such as soil–plant–atmosphere sensors and multispectral images obtained from unmanned aerial systems (UASs) are widely used to improve crop management. Multispectral aerial imagery is often used to calculate vegetation indices (VI) as combinations of the proportion of light reflected by leaves in the visible, red-edge, and near-infrared parts of the electromagnetic spectrum [5]. Among the most commonly used vegetation indices are the normalized difference vegetation index (NDVI), the optimized soil adjusted vegetation index (OSAVI), the difference vegetation index (DVI), and the normalized difference red edge index (NDRE). NDVI is the most commonly used UAS-based index, is directly related to the chlorophyll content and plant health and, in horticultural crops and fruit trees, it has been reported to act as a robust water stress indicator. A new vegetation index is proposed, the transformed red range vegetation index (TRRVI), which was developed using the high spectral resolution of the hyperspectral data at the “red” and “red edge” between 660 and 735 nm and the NDVI
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
