Abstract
Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.
Highlights
One of the primary goals of precision agriculture is identifying intra-field variability to enable a management response to plant stress that will maximize yield
Our results suggest that an important relationship exists between relative canopy shape and crop nutrition that can be leveraged to improve the usefulness of low cost unmanned aerial systems (UAS) for precision agriculture
We developed a methodology for extracting crop canopy characteristics from high-resolution, photogrammetric canopy surface models and demonstrated how these characteristics can be used to improve remote monitoring of crop nutrient status
Summary
One of the primary goals of precision agriculture is identifying intra-field variability to enable a management response to plant stress that will maximize yield. Remote sensing is increasingly being used as an alternative tool for crop monitoring that more accurately accounts for spatial variations in crop stress across a field [1]. Symptoms of deficiencies of essential macronutrients, like nitrogen (N), phosphorus (P), and potassium (K), can often be differentiated visually, making remote identification possible. These nutrients perform crucial roles in plant development and are needed in relatively high concentrations at specific plant growth stages [8]
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