Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

Abstract

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

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

“…Although some cultivars have shown a completely different trend, not in line with the spectroradiometric responses of most cultivars. Many authors, such as Hassan et al [17] and Duan et al [19], Marino et al, [41] report the rapid assessment of VIs from UAV dynamic monitoring and predicting of the biomass change and grain yield during the growing season of wheat. In the present study, a significant correlation between agronomic parameters were revealed for each cultivar and VIs, but showed some limitations and weaknesses in the inter-cultivar analysis, since each cultivar has a specific own spectral response and function yield-VIs as stated before.…”

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

“…Experience has demonstrated that sampling errors cause difficulty in detecting prominent differences among samples and destructive sampling reduces the plot area for estimating final biomass and grain yield. Proximal canopy sensing can be used to estimate crop N status and crop biomass without destructive sampling and thus has the potential to provide a fast, inexpensive, and accurate technique to estimate plant biomass production [5,6] and grain yield [7] of genotypes. The indestructive assessment of grain yield prior to harvest would be beneficial for crop breeders in a superior cultivar selection process [2,4,8,9].…”

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

“…Using multispectral and hyperspectral cameras equipped on a satellite or aircraft, images could be used to calculate various vegetation indices which can indicate variabilities in the field. Vegetation indices, such as the normalized difference vegetation index (NDVI), rely on comparing light intensities reflected from canopies in the visual and near infra-red (NIR) range [6,7]. Given the vast distance from a satellite to a field and the area an image can cover, resulting images often have high temporal resolution and low spatial resolution compared to other methods [8].…”

“…As these UAVs began to incorporate more peripheral technologies and grew in complexity, a new term was developed to describe the whole system together-unmanned aircraft system (UAS) [10,11]. Along with this new technology, came many new challenges such as processing of geospatial data [12][13][14] and lower temporal resolutions when applied to large areas of land [7,15]. Therefore, it should be noted that UAS technology is not meant to replace satellite data, as there are trade-offs for using one over the other.…”