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High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes

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Abstract

Plant emergence and stand establishment are key indicators of early crop development that are routinely assessed in potato agronomy and crop improvement research. The standard method for evaluating emergence is through manual plant counts at regular intervals. In this proof-of-concept study, unmanned aerial vehicles integrated with multispectral imaging were used for high-throughput evaluation of crop emergence under field conditions. High-resolution aerial imaging was performed at 15 m above ground level to capture data from potato plots of two varieties (‘Alturas’ and ‘Payette Russet’) in which the seed had been treated with different concentrations of growth regulators (including non-treated controls). The treatments resulted in differences in plant emergence and establishment. The images were collected at 32, 37, and 43 days after planting (DAP). Image-based features such as plant count, SUM-NDVI, and SUM-BINARY were computed from normalized difference vegetation index (NDVI) images for each treatment plot using ArcGIS®. The Pearson’s correlation coefficients (r) were significant (p < 0.05) between image-based plant counts (r = 0.82) and SUM-NDVI (r = 0.62-0.73) with that of manual plant counts for both varieties, especially at early growth stages (32 DAP) when differences in emergence among treatments were more pronounced. The treatment effects on plant emergence and establishment were effectively resolved in the aerial multispectral images. Selection of the pertinent polygon threshold area to eliminate noise in delineating individual plants during image processing was important for resolution of treatment effects. The data shows that the technique can be applied in potato establishment evaluation.

Resumen

La emergencia y el establecimiento de plantas son indicadores clave en el desarrollo temprano de un cultivo, siendo variables comúnmente evaluadas en investigaciones para mejoramiento genético del cultivo de papa. El método normalmente utilizado para evaluar emergencia es el conteo manual de plantas en intervalos regulares. Para evaluar emergencia en condiciones de campo, en este “estudio de prueba de concepto”, se utilizó un sistema de alto rendimiento constado por una aeronave remotamente piloteada con una cámara multiespectral integrada. Se tomó imágenes de alta resolución a 15 m de altura para capturar datos de parcelas de dos variedades de papa (“Alturas” y “Payette Russet”), cuyas semillas fueron tratadas con diferentes concentraciones de reguladores de crecimiento (incluyendo también testigos sin tratamiento). Los tratamientos mostraron diferencias en emergencia y establecimiento de plantas. Se colectó imágenes a 32, 37, y 43 días después de la siembra (DDS). Se utilizó el programa ArcGIS® para obtener el NDVI (índice de vegetación de diferencia normalizada) de cada parcela, a partir del cual se obtuvo el número de plantas, SUM-NDVI y SUM-BINARY. Se encontró correlación significativa (p < 0.05) entre el conteo de plantas basado en imágenes (r = 0.82) y en SUM-NDVI (r = 0.62-0.73), con respecto al conteo manual para ambas variedades, especialmente en los estadios tempranos de crecimiento (32 DDS), cuando las diferencias en emergencia entre tratamientos eran más pronunciadas. Los efectos del tratamiento en emergencia y establecimiento fueron efectivamente detectados con las imágenes aéreas multiespectrales. Fue importante seleccionar adecuadamente el área de los polígonos utilizados para disminuir el ruido delimitando las zonas cubiertas por el cultivo. Los datos muestran que la metodología presentada se puede aplicar en la evaluación del establecimiento del cultivo de papa.

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Acknowledgements

The authors thank Dr. Lav R. Khot, Center for Precision and Automated Agricultural Systems, Washington State University for his assistance in aerial data collection. This activity was funded, in part, with USDA National Institute for Food and Agriculture, Hatch Project, 1002864 (WNP00821).

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Correspondence to Sindhuja Sankaran.

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Sindhuja Sankaran and Juan José Quirós are equal contributing authors.

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Sankaran, S., Quirós, J.J., Knowles, N.R. et al. High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes. Am. J. Potato Res. 94, 658–663 (2017). https://doi.org/10.1007/s12230-017-9604-2

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