TY - JOUR
T1 - Quantitative potato tuber phenotyping by 3D imaging
AU - Liu, Jiangang
AU - Xu, Xiangming
AU - LIU, YONGHUAI
AU - Rao, Zexi
AU - Smith, Melvyn L.
AU - Jin, Liping
AU - Li, Bo
N1 - Funding Information:
This research was financially supported by the National Natural Science Foundation of China ( 32001485 ), Breeding new varieties for advantageous agricultural industries in Ningxia - Digital breeding system for potato in China ( 2019NYYZ01-4 ), and National Key Research and Development Program of China ( 2020YFD1000804-3 ).
Publisher Copyright:
© 2021 IAgrE
PY - 2021/10/1
Y1 - 2021/10/1
N2 - The accurate phenotyping of the external quality attributes of potato tubers is important in potato breeding. Currently, the assessment of potato tuber shape, together with eye density and depth, are based on subjective naked eye visual evaluation. However, such a manual visual assessment makes it very difficult to reliably phenotype these and other important, more complicated, geometrical traits, such as shape uniformity. In this study, a 3D image analysis method has been developed for counting potato eyes and estimating eye depth based on an evaluation of the curvature of an acquired 3D point cloud. Six shape uniformity-related traits, together with their shape indices (SI), were measured for six potato varieties. These were 26 collected from three field experiments designed initially to study the effects of variation in nitrogen (N), potassium (K) and compound fertilisers along with tuber mass, on all investigated external traits. We demonstrate that a 3D image analysis technique can estimate the number of potato eyes and their depth with a high degree of accuracy. In addition, three shape uniformity traits were identified as offering a better power discrimination between varieties. The preliminary experiment found potato tuber mass to significantly affect both the shape uniformity and eye count, while fertiliser treatments showed no effect on all traits except SI. However, further investigation with a larger sample size is required for confirmation.
AB - The accurate phenotyping of the external quality attributes of potato tubers is important in potato breeding. Currently, the assessment of potato tuber shape, together with eye density and depth, are based on subjective naked eye visual evaluation. However, such a manual visual assessment makes it very difficult to reliably phenotype these and other important, more complicated, geometrical traits, such as shape uniformity. In this study, a 3D image analysis method has been developed for counting potato eyes and estimating eye depth based on an evaluation of the curvature of an acquired 3D point cloud. Six shape uniformity-related traits, together with their shape indices (SI), were measured for six potato varieties. These were 26 collected from three field experiments designed initially to study the effects of variation in nitrogen (N), potassium (K) and compound fertilisers along with tuber mass, on all investigated external traits. We demonstrate that a 3D image analysis technique can estimate the number of potato eyes and their depth with a high degree of accuracy. In addition, three shape uniformity traits were identified as offering a better power discrimination between varieties. The preliminary experiment found potato tuber mass to significantly affect both the shape uniformity and eye count, while fertiliser treatments showed no effect on all traits except SI. However, further investigation with a larger sample size is required for confirmation.
KW - 3D image analysis
KW - phenotyping
KW - curvature estimation
KW - shape uniformity
KW - potato eye
KW - potato shape
KW - Curvature estimation
KW - Shape uniformity
KW - Potato shape
KW - Phenotyping
KW - Potato eye
UR - http://www.scopus.com/inward/record.url?scp=85112771556&partnerID=8YFLogxK
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UR - https://www.mendeley.com/catalogue/6963e473-4227-3c67-afae-8dc3f4b18e9b/
U2 - 10.1016/j.biosystemseng.2021.08.001
DO - 10.1016/j.biosystemseng.2021.08.001
M3 - Article (journal)
SN - 1537-5110
VL - 210
SP - 48
EP - 59
JO - Biosystems Engineering
JF - Biosystems Engineering
ER -