A Novel Auto-Sorting System for Chinese Cabbage Seeds

Huang, Kuo-Yi; Jianfeng, Cheng

doi:10.3390/s17040886

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…(15) The three defined points of the branches are as follows ( Fig. 7): (1) endpoint: only one pixel belongs to the 8-adjacency matrix, (2) branch point: three or four pixels belong to the 8-adjacency matrix rather than to the 4-adjacency matrix, (3) normal point: only two pixels belong to the 8-adjacency matrix. The skeleton pruning method was used for removing redundant branches and obtaining representative skeletons.…”

Section: Cotyledons and Root System Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance

Xu¹,

Luo²,

Liao³

et al. 2020

Sensors and Materials

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Section: Cotyledons and Root System Extractionmentioning

confidence: 99%

“…Image processing is a powerful and widely used method for inspecting agricultural products. Huang and Cheng (3) presented an auto-sorting system with machine vision for Chinese cabbage seeds. Shape, color, and texture were the features used to sort seeds according to quality by using a neural network.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance

Xu¹,

Luo²,

Liao³

et al. 2020

Sensors and Materials

Self Cite

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“…In [7], 16 morphological features were extracted to classify dry beans, and the overall correct classification rate of SVM was 93.13%. In [8], developed a machine that automatically extracts shape, color, and texture feature data of cabbage seeds and uses them to classify the quality of seeds. The research of maize seeds has focused on bioactivity screening and quality inspection.…”

Section: Introductionmentioning

confidence: 99%

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Tan

Zhang

et al. 2022

Agriculture

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Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

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“…Some scholars have extracted feature parameters based on color and shape to classify seeds by variety, and their proposed research model could effectively predict the germination rate of pepper seeds (Granitto, Verdes, & Ceccatto, 2005; Tu et al, 2018). Huang used machine vision to extract the shape, color, and texture features of seeds to automatically classify the quality of cabbage seeds (Huang & Cheng, 2017). Kiratiratanapruk used color and texture features to classify the quality of more than 10 types of seeds (Kiratiratanapruk & Sinthupinyo, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…However, with the increasing number of varieties of maize seed in China, their color characteristics overlap, so an accurate evaluation of their appearance cannot be achieved solely using spectral analysis. Nonetheless, machine vision can provide a method to classify maize by perceiving images, interpreting, and identifying marks, which can achieve the same recognition and classification effects as human vision (Huang & Cheng, 2017; Sabanci, Kayabasi, & Toktas, 2017; Tu, Li, Yang, Wang, & Sun, 2018; Xie, Wang, & Yang, 2019). To eliminate the unfavorable situation caused by the above factors, this research will investigate a method to obtain high‐quality maize seeds for the market and producers.…”

Section: Introductionmentioning

confidence: 99%

Varietal classification of maize seeds using computer vision and machine learning techniques

Yang

Zeng

et al. 2021

J Food Process Engineering

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In agriculture, seed sorting is critical for production and marketing purposes. Low‐quality seeds can cause poor plant growth and lead to problems such as disease and low yields. This study uses machine vision and machine learning to develop a rapid detection and classification method for maize (Zea mays L.) seeds based on variety purity. A computer vision system was designed to recognize five varieties of maize seeds. Halogen lamps were applied for illumination and a high‐resolution RGB camera was used to acquire images of 8,080 maize seeds in the laboratory. An image processing algorithm was proposed to extract 16 important features (12 dimensional and 4 of shape) from the maize seed images, and a user‐friendly interface was developed using a MATLAB graphical user interface (GUI). Multilayer perceptron (MLP), decision tree (DT), linear discrimination (LDA), Naive Bayes (NB), support vector machine (SVM), k‐nearest neighbors (KNN), and AdaBoost algorithm were used to develop the varietal classification model. The optimal model parameters were obtained with 10‐fold cross‐validation, and the performance metrics were compared. The names of the maize varieties were marked in the GUI. The overall classification accuracy was determined as 96.26, 94.95, 95.97, 93.97, 96.46, 95.59, and 95.31% for MLP, DT, LDA, NB, SVM, KNN, and AdaBoost, respectively. The SVM classification model obtained the highest accuracy for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 93.07, 98.95, 96.15, 89.65, and 99.22%, respectively. The classification results satisfy the needs of producers and consumers.

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A Novel Auto-Sorting System for Chinese Cabbage Seeds

Cited by 17 publications

References 18 publications

Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance

Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Varietal classification of maize seeds using computer vision and machine learning techniques

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