Abstract
BACKGROUND: Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. RESULTS: Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. CONCLUSIONS: This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
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Claims 4
Claim · model-written summary
HSI with MLP distinguished Jingke 968 from varieties differing at as few as two loci with at least 99% accuracy.
- Material
- Jingke 968e0
- Treatment
- hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP)e0
- Outcome
- accuracye0
- Value
- 99% or highere0
- Comparator
- varieties that differed by as few as two locie0
Zea maysNot reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize variety seeds”
e0“hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy”
Claim · model-written summary
HSI with SVM distinguished Jingke 968 from varieties differing at as few as two loci with at least 99% accuracy.
- Material
- Jingke 968e0
- Treatment
- support vector machine (SVM) modele0
- Outcome
- accuracye0
- Value
- 99% or highere0
- Comparator
- varieties that differed by as few as two locie0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize variety seeds”
e0“hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy”
Claim · model-written summary
Machine vision imaging achieved about 90% accuracy.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize variety seeds”
e0“machine vision imaging provided ~ 90% accuracy”
Claim · model-written summary
Validation and updating produced a Jingke 968 genuineness model that discriminated genetically similar and distant varieties.
- Material
- Jingke 968e0
- Outcome
- effectively discriminated between genetically similar and distant varietiese0
- Comparator
- varieties not included in the training datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize variety seeds”
e0“Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties.”