Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

Zhang, Chu; Zhao, Yiying; Yan, Tianying; Bai, Xiulin; Xiao, Qinlin; Gao, Pan; Li, Mu; Huang, Wei; Bao, Yukun; He, Yong; Liu, Fei

doi:10.1016/j.infrared.2020.103550

Cited by 67 publications

(25 citation statements)

References 37 publications

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“…In the case of the Earbox, even research structures with limited resources, farmer cooperatives, or multi-site research projects (limited by multiple observers and non-standardized methodologies), can claim reliable and reproducible ear phenotyping data with a system that can be easily modified to be integrated into complete ear and processing chains. For example, cameras can be replaced for higher resolutions or multispectral acquisition for characterization of grain physiology (Caporaso et al, 2018;Chu et al, 2020;Pang et al, 2020;Türker-Kaya & Huck, 2017;C. Zhang et al, 2020;J.…”

Section: Discussionmentioning

confidence: 99%

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Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Oury¹,

Leroux²,

Turc

et al. 2021

Preprint

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Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

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Section: Discussionmentioning

confidence: 99%

“…It is a highly flexible, trainable framework that has been widely validated in many scientific domains, including plant science (Chipindu et al, 2020;Davis et al, 2020;Ganesh et al, 2019;Machefer et al, 2020;Wang et al, 2019;C. Zhang et al, 2020;J.…”

Section: A Simple and Low -Cost Image Acquisition Systemmentioning

confidence: 99%

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Oury¹,

Leroux²,

Turc

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…In recent years, as a representative of deep learning technology, convolutional neural networks (CNNs) develop rapidly and are widely used for image recognition ( Afonso et al, 2019 ; Altuntaş et al, 2019 ; Gao et al, 2020 ; Zhang C. et al, 2020 ). Compared with traditional machine learning technology, CNNs are naturally embedded with a feature learning part through the combination of low-level features to form more abstract high-level features.…”

Section: Introductionmentioning

confidence: 99%

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

et al. 2022

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Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.

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“…Due to the fact that hyperspectral image is a three-dimension (3D) data cube, one-dimension (1D), two-dimension (2D) and 3D data can be extracted from hyperspectral images. The corresponding 1D (Audebert et al, 2019;Zhang et al, 2020), 2D (Wang et al, 2018;Audebert et al, 2019), and 3D (Audebert et al, 2019;Nagasubramanian et al, 2019) deep learning models can be developed for hyperspectral image analysis. Nowadays, deep learning has been used in fruit quality and safety inspection by hyperspectral imaging, such as bruises on winter jujube (Feng et al, 2019b), strawberry ripeness (Gao et al, 2020), and early decay on blueberry (Qiao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Zhang

Yan

et al. 2021

Front. Plant Sci.

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Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

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Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

Cited by 67 publications

References 37 publications

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

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