Research on physicochemical properties, microscopic characterization and detection of different freezing-damaged corn seeds

Zhang, Jun; Wang, Zhiying; Qu, Moyuan; Fang, Cheng

doi:10.1016/j.fochx.2022.100338

Cited by 6 publications

(6 citation statements)

References 35 publications

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“…These equipments are generally expensive and complicated to use 39 . And the obtained data needs to be further corrected, for the purpose of effective wavelength spectrum extraction, database building and other operations [40][41][42] . Even if RGB images are used for classi cation, traditional machine learning classi cation methods such as SVM, MLP, and KNN 43 require complex image preprocessing to extract features.…”

Section: Discussionmentioning

confidence: 99%

Classification and Variety Identification of Corn Ears using Machine Vision combined with Convolutional Neural Network

Jinpu

Zhao

et al. 2022

Preprint

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Corn is an important human food crop and animal feed source. Purity of corn seed is critical to yield and marketing. Screening of corn ears is an important but time-consuming and labor-intensive task in seed production. In recent years, deep learning has made great achievements in tasks such as image classification, object detection, face recognition, etc. In this paper , a method combining convolutional neural network VGG16 and machine vision to quickly classify different varieties of corn using corn ear images is proposed. By collecting RGB images of corn ears with intact phenotypic traits of 5 varieties, a data set containing 1000 images was constructed, and divided into training set, validation set and test set according to the ratio of 7:2:1. By improving the fully connected layer structure of the VGG16 network, optimizing the training parameters, and using transfer learning and data enhancement techniques, the optimal performance model was obtained after training all layers of the VGG16, and the accuracy rate reached 98.00% on the test set. Under the same experimental conditions, comparing the three methods of training from scratch, pre-feature extraction and only training the fully connected layer, the accuracy rates obtained were 94.00%, 96.88%, and 94.00%, respectively. The improved model achieved the highest classification accuracy rate and stable performance. In the experiments, the effects of network parameters on the model classification results were also discussed. The experiment showed that the phenotypic characteristics of the corn ears could better realize the classification and identification of different varieties of corn, which provides a reference for the intelligent sorting of corn seed production.

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Section: Discussionmentioning

confidence: 99%

Classification and Variety Identification of Corn Ears using Machine Vision combined with Convolutional Neural Network

Jinpu

Zhao

et al. 2022

Preprint

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“…MDA content was analyzed at 530 nm by the TBA method [ 62 ]. CAT activity was measured according to the method described by Zhang et al [ 63 ]. SOD activity was determined with nitroblue tetrazolium (NBT) [ 64 ].…”

Section: Methodsmentioning

confidence: 99%

Isolation and Functional Analysis of VvWRKY28, a Vitis vinifera WRKY Transcription Factor Gene, with Functions in Tolerance to Cold and Salt Stress in Transgenic Arabidopsis thaliana

Liu

Liang

Cai

et al. 2022

IJMS

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The grape (Vitis vinifera L.) not only has a long history of cultivation, but also has rich nutritional value and high economic value. However, grapes often face many threats in the growth process. For example, low temperature and salt stress restrict the growth status, yield, and geographical distribution of grapes. WRKY, as one of the largest transcription factor (TF) families in plants, participates in the response of plants to stress. VvWRKY28, a new zinc finger type transcriptional regulator gene, was isolated from Beichun (V. vinifera×V.amurensis) in this study. From the subcellular localization results, it can be concluded that VvWRKY28 was localized in the nucleus. The expression of VvWRKY28 was enriched in leaves (young and mature leaves), and cold and high salt conditions can induce high expression of VvWRKY28. After being transferred into Arabidopsis, VvWRKY28 greatly improved the tolerance of Arabidopsis to low temperature and high salt and also changed many physiological and biochemical indicators of transgenic Arabidopsis to cope with cold and high salt stimulation. The content of malondialdehyde (MDA) was decreased, but for chlorophyll and proline, their content increased, and the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were improved. In addition, under cold stress, binding with cis-acting elements promotes the expression of downstream genes related to cold stress (RAB18, COR15A, ERD10, PIF4, COR47, and ICS1). Moreover, it also plays an active role in regulating the expression of genes related to salt stress (NCED3, SnRK2.4, CAT2, SOD1, SOS2, and P5CS1) under salt stress. Therefore, these results provide evidence that VvWRKY28may play a role in the process of plant cold and salt stress tolerance.

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“…It also proposes a control algorithm for the program. The algorithm for controlling physical processes for use in research work was also developed by other authors [18,19,20]. An algorithm for the treatment of moistened seeds of agricultural plants with electric current has been developed (Figure 10).…”

Section: When Measuring the Load Resistance Of Three Phasesmentioning

confidence: 99%

Electrophysical processes in an electroterminator (with an intelligent system) with sliding contacts in the power circuit

Denmukhammadiev

Mukhammadiev

Kucharov

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This article presents data on the properties of sliding contacts during the rotation of the axis of a device for pre-sowing treatment of crop seeds, considers the issues of automation of intermittent power supply and measurement of electrical conductivity and temperature parameters of seed material in the process of electrotechnology. At the same time, the contact resistance of the power circuit, normalized moistening of sowing seeds, depending on the type of plants, was taken as the basis of the physical model. Smart system takes into account soil parameters (type, density, moisture content). In the initial period of seed treatment, moisture penetrates into the seed from the outside. therefore, the time of humidification and electrical treatment are the main factors. Depending on the seed variety, it will be possible to change the operating parameters of the electrical treatment of crop seeds. The work also carried out a review of the literature and mathematical calculations.

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Research on physicochemical properties, microscopic characterization and detection of different freezing-damaged corn seeds

Cited by 6 publications

References 35 publications

Classification and Variety Identification of Corn Ears using Machine Vision combined with Convolutional Neural Network

Classification and Variety Identification of Corn Ears using Machine Vision combined with Convolutional Neural Network

Isolation and Functional Analysis of VvWRKY28, a Vitis vinifera WRKY Transcription Factor Gene, with Functions in Tolerance to Cold and Salt Stress in Transgenic Arabidopsis thaliana

Electrophysical processes in an electroterminator (with an intelligent system) with sliding contacts in the power circuit

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