Next Article in Journal
Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint
Previous Article in Journal
A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images
Previous Article in Special Issue
Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali
Open AccessArticle

Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices

Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, I-86100 Campobasso, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 249; https://doi.org/10.3390/rs12020249
Received: 11 November 2019 / Revised: 9 December 2019 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification. View Full-Text
Keywords: vegetation indices; crop yield; precision agriculture; wheat crop; cluster analysis validation vegetation indices; crop yield; precision agriculture; wheat crop; cluster analysis validation
Show Figures

Figure 1

MDPI and ACS Style

Marino, S.; Alvino, A. Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices. Remote Sens. 2020, 12, 249.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop