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Article

Applications of High-Resolution Imaging for Open Field Container Nursery Counting

1
The Climate Corporation, San Francisco, CA 94103, USA
2
School of Engineering, University of California, Merced, CA 95343, USA
3
Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA
4
School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Virginia Beach, VA 23455, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 2018; https://doi.org/10.3390/rs10122018
Received: 17 October 2018 / Revised: 3 December 2018 / Accepted: 6 December 2018 / Published: 12 December 2018
(This article belongs to the Special Issue Remote Sensing in the Age of Electronic Ecology)
Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm for all cases. In this paper, the development of classification methods was confined to three representative categories: green foliage, yellow foliage, and flowering plants. Vegetation index thresholding and the support vector machine (SVM) were used for classification. Classification accuracies greater than 97% were obtained for each case. Based on the classification results, an algorithm based on canopy area mapping was built for counting. The effects of flight altitude, container spacing, and ground cover type were evaluated. Results showed that container spacing and interaction of container spacing with ground cover type have a significant effect on counting accuracy. To mimic the practical shipping and moving process, incomplete blocks with different voids were created. Results showed that the more plants removed from the block, the higher the accuracy. The developed algorithm was tested on irregular- or regular-shaped plants and plants with and without flowers to test the stability of the algorithm, and accuracies greater than 94% were obtained. View Full-Text
Keywords: image processing; container nursery; inventory management; counting; overlap separation image processing; container nursery; inventory management; counting; overlap separation
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MDPI and ACS Style

She, Y.; Ehsani, R.; Robbins, J.; Nahún Leiva, J.; Owen, J. Applications of High-Resolution Imaging for Open Field Container Nursery Counting. Remote Sens. 2018, 10, 2018. https://doi.org/10.3390/rs10122018

AMA Style

She Y, Ehsani R, Robbins J, Nahún Leiva J, Owen J. Applications of High-Resolution Imaging for Open Field Container Nursery Counting. Remote Sensing. 2018; 10(12):2018. https://doi.org/10.3390/rs10122018

Chicago/Turabian Style

She, Ying, Reza Ehsani, James Robbins, Josué Nahún Leiva, and Jim Owen. 2018. "Applications of High-Resolution Imaging for Open Field Container Nursery Counting" Remote Sensing 10, no. 12: 2018. https://doi.org/10.3390/rs10122018

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