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Remote Sens. 2018, 10(12), 1895; https://doi.org/10.3390/rs10121895

Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data

1
Research Institute for Automotive Diagnosis Technology of Multi-scale Organic and Inorganic Structure, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si, Gyeongsangbuk-do 37224, Korea
2
College of Science & Engineering, Texas A&M University Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA
3
Texas A&M AgriLife Research and Extension Center, 10345 TX-44, Corpus Christi, TX 78406, USA
*
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 23 November 2018 / Accepted: 25 November 2018 / Published: 27 November 2018
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Abstract

Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid. View Full-Text
Keywords: UAV; object-based analysis; open boll detection; yield estimation; agriculture; cotton UAV; object-based analysis; open boll detection; yield estimation; agriculture; cotton
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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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Yeom, J.; Jung, J.; Chang, A.; Maeda, M.; Landivar, J. Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data. Remote Sens. 2018, 10, 1895.

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