Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra

Yu, Lina; Liu, Wenjie; Li, Weijun; Qin, Hong; Xu, Jian; Zuo, Min

doi:10.1016/j.biosystemseng.2018.05.011

Cited by 25 publications

(12 citation statements)

References 29 publications

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“…Yu et al 5 identified corn haploid seeds using near infrared (NIR) spectroscopy, which is non-destructive, fast, and is a low-cost technology. A non-linear analytical method was proposed to identify haploid corn seeds.…”

Section: Introductionmentioning

confidence: 99%

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

2019

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Background The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

2019

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“…The spectral analysis method reflects the difference in the internal physical structure and the chemical composition of seed varieties through spectral information, which has the advantages of being fast, accurate, and nondestructive [12]; it has been applied to multiple tasks related to seeds, such as the identification of soybean [13,14], rice [6] and maize [15][16][17] seed varieties, the identification of nontransgenic and transgenic seeds [18][19][20], the identification of seed geographical sources [21], the identification of infected germ seeds [22], the identification of infected pest seeds and healthy seeds [23], identification of the year of seeds [24,25], and the determination of tomato [26], soybean [27,28], corn [28], muskmelon [29], cabbage and radish [30] seed vitality. Less research has been performed on image analysis than spectral analysis, and only some plants have been studied through image analysis, such as peppers [31], paddy [32,33], and corn [34].…”

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confidence: 99%

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

et al. 2019

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Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.

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“…In each group, the data from a different male parent were employed as D S data, and the progeny data of haploid and hybrid was used as D T data. The spectra data of seeds were acquired by a cross-interval way under the stable instrument ( Figure 3) and environment after calibration (Lin et al, 2018;Yu et al, 2018). The collection lasted for 10 days, and is an uncooled InGaAs diode array with a resolution of 6.194355nm, and its wavelength range is between 900 nm and 1700 nm.…”

Section: Dataset Of Nir-dt Spectrummentioning

confidence: 99%

“…The NIR has the advantages of being non‐destructive (Jiang et al, ; Pasquini, ; Rhiel, Cohen, Murhammer, & Arnold, ; Sandora et al, ), real‐time (Alves‐Rausch, Bienert, Grimm, & Bergmaier, ; Blanco & Villarroya, ; Sandnes et al, ; Sato, Zahlner, Berghofer, Lošák, & Vollmann, ) and of less cost (Sandora et al, ) than other methods, like nuclear magnetic resonance (NMR) (Chen & Song, ), weighting (Smelser et at., ) and image (De La Fuente et al, ). Through the diffuse transmittance (DT) mode, the spectra of non‐uniform organism seeds can be effectively collected with less disturbing effects from other side of seed (Qin et al, , ), and this improvement shows a high performance of seed identification (Li & Li, ; Lin, Yu, Li, & Qin, ; Yu et al, ). However, due to a great variety of seed parents, the identification models need to be established independently for different parents' progeny.…”

Section: Introductionmentioning

confidence: 99%

“…2003), weighting (Smelser et at., 2015) and image (De La Fuente et al, 2017). Through the diffuse transmittance (DT) mode, the spectra of non-uniform organism seeds can be effectively collected with less disturbing effects from other side of seed (Qin et al, 2015(Qin et al, , 2016, and this improvement shows a high performance of seed identification Lin, Yu, Li, & Qin, 2018;Yu et al, 2018). However, due to a great variety of seed parents, the identification models need to be established independently for different parents' progeny.…”

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confidence: 99%

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A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Lin

et al. 2019

Plant Breeding

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Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.

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Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra

Cited by 25 publications

References 29 publications

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

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