Abstract
To enable automatic transplantation of plug seedlings and improve identification accuracy, an algorithm to identify ideal seedling leaf sets based on Fourier descriptors is developed, and a classification method based on expert system is adopted to improve the identification rate of the plug seedlings. First, the image of the plug seedlings is captured by image acquisition system, followed by application of K-means clustering for image segmentation and binary processing and identification of the ideal seedling leaf set by Fourier descriptors. Then we obtain feature vectors, such as gray scale (R+B+G)/3, hue H, and rectangularity. After that the knowledge model of the plug seedlings is defined, and the inference engine based on knowledge is designed. Finally, the recognizing test is carried out. The success rate of the identification of 10 varieties of plug seedlings from 190 plates is 98.5%. For the same sample, the recognizing rate of support vector machine (SVM) is 85%, the recognizing rate of particle-swarm optimization SVM (PSOSVM) is 87%, the recognizing rate of back propagation neural network (BP) is 63%, and the recognizing rate of Fourier descriptors SVM (FDSVM) is 87%. These results show that our recognition method based on an expert system satisfies the requirements of automatic transplanting.
Highlights
Plug-seedling cultivation was developed in Europe and America in the 1970s and has been rapidly enhanced in recent years because of its advantages, including mechanized operation, low cost, high survival rates, and easy transport [1,2,3]
190 plates and 10 types of seedlings are divided into training and test samples, with 10 samples selected as training samples among all of the plug seedlings
Our findings show that feature vectors significantly influenced seedling identification and that an ideal seedlingleaf set was obtained using a Fourier-descriptor clustering
Summary
Plug-seedling cultivation was developed in Europe and America in the 1970s and has been rapidly enhanced in recent years because of its advantages, including mechanized operation, low cost, high survival rates, and easy transport [1,2,3]. Reference [6,7,8] showed that according to seedling characteristics, color features associated with the super green method (2G-R-B) could be applied to image segmentation, resulting in good results, the effects of serious overlaps between the seedlings represent a limitation. A support vector machine (SVM) was subsequently used to identify the leaf disease associated with honey pomelo, with recognition accuracy of 94.16%. These intelligent-recognition methods mainly extract the shape, color, texture, and other information from seedlings by vision, followed by their classification using a neural network. We confirm the accuracy of the method through practical application
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