Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

Bai, Xiulin; Zhang, Chu; Xiao, Qinlin; He, Yong; Bao, Yukun

doi:10.1039/c9ra11047j

Cited by 27 publications

(11 citation statements)

References 34 publications

(35 reference statements)

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“…Previous studies have researched the non-destructive identification of seed varieties based on hyperspectral imaging and machine learning or deep learning [ 4 , 9 , 17 , 18 , 24 – 26 , 48 , 49 ]. Although there might be a certain distance from the actual application due to a limited number of varieties in the training set, the successes of these studies guide the seed variety genuineness detection to ensure seed purity.…”

Section: Discussionmentioning

confidence: 99%

“…This method can effectively differentiate and classify target objects or predict crop traits by detecting subtle differences in chemical composition and distribution [ 17 – 20 ]. Moreover, considerable evidence indicates that spectral characteristics are genotype-specific and can be used to distinguish plant genotypes [ 21 ], suggesting the feasibility of identifying crop varieties by hyperspectral imaging [ 17 , 22 – 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Hyperspectral image processing combined with machine learning algorithms has been used to classify the varieties of maize seeds according to differences in their chemical composition [ 26 , 36 – 38 ]. More details for those varieties of classification tasks are shown in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

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A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Wen

Cheng

et al. 2022

Plant Methods

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Wen

Cheng

et al. 2022

Plant Methods

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“…The results showed the effectiveness of feature reduction. Bai et al (111) applied HSI (874-1,734 nm) to classify four varieties of common maize seed and four varieties of silage maize seeds. This work visualized hyperspectral images of the first six PCs of eight varieties of maize seed, which indicated that there were differences among different varieties of maize seeds.…”

Section: Application Of Hyperspectral Imaging To Trace Food Varieties and Geographical Origins Crop Foodmentioning

confidence: 99%

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Liu

Zhu

et al. 2021

Front. Nutr.

Self Cite

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Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

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“…The third type of methodology involves scanning the placed total layer of seeds with further analysis of their size using programs to analyse the fractionality of the image (Dilip & Sahu, 2017;Unajan et al, 2019;Bai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

Tsytsiura

2021

Eng. Agríc.

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Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

Abstract: NIR-HSI and chemometrics were used to identify different varieties of common and silage maize seeds.

Cited by 27 publications

References 34 publications

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

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