Abstract
Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.
Highlights
Crop lodging refers to the bending over or displacement of the aboveground stalk from the upright stance, or damage of the root-soil attachment
The main objectives of this study are: (1) to introduce a comprehensive methodology and workflow to investigate the potential for maize lodging detection within individual rows based on the canopy structure and anomaly information obtained from low-altitude, hyperspatial unmanned aircraft system (UAS) images, and SfM photogrammetry; and (2) to present a direct and quantitative accuracy assessment of maize lodging at an individual row scale from the open field
Instead of manual scouting, the proposed UAS-imaging method has the potential to be standardized as a workflow to quantitatively assess lodging severity in a crop field environment, and to provide a rapid assessment of lodging damage following post-storm events
Summary
Crop lodging refers to the bending over or displacement of the aboveground stalk from the upright stance (stalk lodging), or damage of the root-soil attachment (root lodging). Recent advances in near-surface photogrammetric techniques have been applied to monitor crop lodging and its effects on yield and grain quality. A functional regression assessment metric was introduced to predict the lodging severity for a rice field based only on a 3-m-high nadir-view image over the field [7]. Techniques of functional data analysis were applied to estimate a regression function and to predict the lodging scores, which ranged from 0
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