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Remote Sens. 2017, 9(9), 923;

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

School of Engineering and Computing Sciences, Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA
Texas A&M AgriLife Research and Extension Center, 10345 State Hwy 44, Corpus Christi, TX 78406, USA
Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd., College Station, TX 77843, USA
This paper is an extended version of a paper entitled “UAS imaging for automated crop lodging detection: a case study over an experimental maize field” presented at SPIE Defense + Commercial Sensing Conference of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA, USA, 10–11 April 2017.
Author to whom correspondence should be addressed.
Received: 5 June 2017 / Revised: 30 August 2017 / Accepted: 31 August 2017 / Published: 4 September 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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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. View Full-Text
Keywords: unmanned aircraft systems; maize lodging; structure-from-motion photogrammetry; crop height modelling; multivariate regression; lodging rate unmanned aircraft systems; maize lodging; structure-from-motion photogrammetry; crop height modelling; multivariate regression; lodging rate

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chu, T.; Starek, M.J.; Brewer, M.J.; Murray, S.C.; Pruter, L.S. Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images. Remote Sens. 2017, 9, 923.

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