Assessment of consumer-grade camera-derived vegetation indices for monitoring nitrogen and leaf relative water content of maize

Abstract

Aim of study: To develop non-destructive and rapid monitoring of water and nitrogen status in maize crops. Area of study: Bu-ali Sina University, Hamedan province, Iran. Material and methods: We used a low-cost modified consumer-grade camera to extract 40 vegetation indices for monitoring leaf N concentrations, SPAD values and relative water content (RWC). In this regard, 528 images taken by the low-cost camera in two consecutive years (2017 and 2018) from maize plants cultivated in a greenhouse under different irrigation and N treatments were evaluated. Main results: Results showed that the best performance outcomes regarding the studied vegetation indices were MCARI, CTVI and CR for SPAD values; MCARI, HUE and CTVI for leaf N concentrations; and TRVI, NDVI and DVI for RWC. In order to increase accuracy of estimated measured data, multiple linear regression equations with combinations of the MCARI, TRVI, NDVI and EVI indices were used. As observed, R2 value was 0.91, 0.60 and 0.90 for SPAD, leaf N concentration and RWC estimation, respectively. Research highlights: The combination of MCARI, TRVI, NDVI and EVI indices provided more accuracy to most of the previous single variable regression models.