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.
Highlights
Monitoring the spatial and temporal variability of wheat within a season is crucial to decision-making in precision farming
Ground agronomic data of each cultivar have been correlated to high-resolution multispectral images (NDVI and optimized soil-adjusted vegetation index (OSAVI))
The correlation of the whole crop cycle data was performed in accordance with van Ittersum et al [31], who states that it is essential to study the relationship among vegetation indices (VIs) and crop traits, to evaluate and estimate the yield potential and the yield gap (Table 1)
Summary
Monitoring the spatial and temporal variability of wheat within a season is crucial to decision-making in precision farming. Precision agriculture is a modern farming management concept using digital techniques to monitor and optimize agricultural production processes. Among the tools used to acquire information, unmanned aerial vehicles (UAVs) equipped with visible and near-infrared cameras, provide, in a fast and easy way, field data for precision agriculture applications [2,3]. The resolution of information from satellite data typically ranges from 5 to 30 m pixels and is unsuitable in agronomy trials given the limitations of real-time monitoring and accuracy [4]. In contrast to satellite imagery and aircraft-based remote sensing, UAVs can be used frequently during the entire growth period [5]. Vegetation indices (VIs) of UAV imagery have the same ability as ground-based recordings to quantify crop responses to experimental treatments [6]. UAVs are a useful technology for crop monitoring at different scales and can be used for agronomic experiments
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