Abstract
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.
Highlights
Maize is one of the most important crops global-wise
The traditional method of detecting seed defects typically relies on manual inspection, which is inefficient and subjective
We devised a study to improve the performance of the model by using a deeper network—GoogLeNet
Summary
Maize is one of the most important crops global-wise. About one-third of the world’s population consumes maize as the major food source. The cultivated land has been decreasing, which is a prominent issue in China. The quality of seeds has become a growing concern for us. The phenotypic defects of seeds are one of the criteria for judging the quality. The traditional method of detecting seed defects typically relies on manual inspection, which is inefficient and subjective. An objective and automated seed screening method is required
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