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Open AccessArticle

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

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Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
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Department of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, KS 66506, USA
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Department of Technology and Development of Corn and Sorghum, Corn Agronomic Modelling; Monsanto Argentina, Pergamino B2700, Argentina
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Department of Agricultural Economics, Kansas State University, 342Waters Hall, Manhattan, KS 66506, USA
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Biological and Agricultural Engineering Department, Kansas State University, Seaton Hall, Manhattan, KS 66506, USA
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PrecisionHawk, 8601 Six Forks Rd #600, Raleigh, NC 27615, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 343; https://doi.org/10.3390/rs10020343
Received: 20 January 2018 / Revised: 19 February 2018 / Accepted: 20 February 2018 / Published: 23 February 2018
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection. View Full-Text
Keywords: unmanned aerial system; supervised learning; corn; farm management; precision agriculture unmanned aerial system; supervised learning; corn; farm management; precision agriculture
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Varela, S.; Dhodda, P.R.; Hsu, W.H.; Prasad, P.V.V.; Assefa, Y.; Peralta, N.R.; Griffin, T.; Sharda, A.; Ferguson, A.; Ciampitti, I.A. Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques. Remote Sens. 2018, 10, 343.

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