Computer Vision as a Complementary Method to Vigour Analysis in Maize Seeds

Medeiros, André Dantas de; Pereira, Márcio Dias; Soares, Tássia Fernanda Santos Neri; Noronha, Bruno Gomes de; Pinheiro, Daniel Teixeira

doi:10.9734/jeai/2018/43464

Cited by 4 publications

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“…Researchers have applied machine vision technology to achieve seed quality testing [1][2][3][4]. Features, such as color, texture, size, and shape, can be extracted from images of seeds, and the defects of the seed can be identified through various classifiers based on computer vision.…”

Section: Introductionmentioning

confidence: 99%

Research on Classification Method of Maize Seed Defect Based on Machine Vision

et al. 2019

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Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture. This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection. In recent years, deep learning has performed well in the field of image recognition. We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms. Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs. 79.2% (SURF+SVM). We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases. We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results. As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.

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Section: Introductionmentioning

confidence: 99%