Research on recognition for cotton spider mites’ damage level based on deep learning
Keywords:
deep learning, cotton spider mites, damage level, MobileNetV1Abstract
The changes in cotton leaf characteristics are closely related to the cotton spider mites’ damage level. Extracting the distinguishable features of cotton leaves is an effective method to identify the level. However, it faces enormous challenges for the classification due to various factors, such as illumination intensity, background complexity, shooting angle and so on. A recognition model is proposed, which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1. The experiments demonstrate that the deep learning model attains the accuracy of 92.29% for the training set and 91.88% for the test set of the mixed data. For testifying the effectiveness of the two-stage training method, the models are trained with the two public datasets, CIFAR-10 and Flowers, and attain the accuracy of 95.46% and 95.57% for the test sets, respectively. The average recognition time for a single cotton leaf image is about 0.015 s. Furthermore, the mobile terminal application is developed with the model embedded, to realize the real-time recognition for cotton spider mites’ damage level in the field. Keywords: deep learning, cotton spider mites, damage level, MobileNetV1 DOI: 10.25165/j.ijabe.20191206.4816 Citation: Yang L L, Luo J, Wang Z P, Chen Y, Wu C C. Research on recognition for cotton spider mites’ damage level based on deep learning. Int J Agric & Biol Eng, 2019; 12(6): 129–134.References
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[3] Guo Y M, Liu Y, Oerlemans A, Lao S Y, Wu S, Lew M S. Deep learning for visual understanding: A review. Neurocomputing, 2016; 187(C): 27–48.
[4] Neha S, Vibhor J, Anju M. An analysis of convolutional neural networks for image classification. Procedia Computer Science, 2018; 132: 377–384.
[5] Kamilaris A, Prenafeta-Boldú F X. Deep learning in agriculture: A survey. Computers & Electronics in Agriculture, 2018; 147(1): 70–90.
[6] Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32–44.
[7] David H, Chris M, Feras D, Niko S, Ben U. Evaluation of features for leaf classification in challenging conditions, IEEE Winter Conference on Applications of Computer Vision, 2015; pp.797–804.
[8] Srdjan S, Marko A, Andras A, Dubravko C, Darko S. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016; 2016(6): 1–11.
[9] Konstantinos P F. Deep learning models for plant disease detection and diagnosis. Computers & Electronics in Agriculture, 2018; 145: 311–318.
[10] He Q H, Ma B X, Qu D Y, Li H W, Wang B. Research of cotton spider mites automatic detection and classification which based on machine vision. Journal of Agricultural Mechanization Research, 2013; 35(4): 152–155. (in Chinese)
[11] Zhang J H, Ji R H, Yuan X, Li H. Recognition of pest damage for cotton leaf based on rbf-svm algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2011; 42(8): 178–183. (in Chinese)
[12] Lu J, Hu J, Zhao G N, Hua M F, Shui Z C. An in-field automatic wheat disease diagnosis system. Computers & Electronics in Agriculture, 2017; 142: 369–379.
[13] Zhou T. An image recognition model based on improved convolutional neural network. Journal of Computational & Theoretical Nanoscience, 2016; 13(7): 4223–4229.
[14] Guillermo L G, Lucas C U, Mónica G L, Pablo M G. Deep learning for plant identification using vein morphological patterns. Computers & Electronics in Agriculture, 2016; 127: 418–424.
[15] Zhu L, Li Z, Li C, Wu J, Yue J. High performance vegetable classification from images based on alexnet deep learning model. Int J Agric & Biol Eng, 2018; 11(4): 217–223.
[16] Yann L, Yoshua B, Geoffrey H. Deep learning. Nature, 2015; 521(7553): 436.
[17] Ian G, Yoshua B, Aaron C. Deep learning. Posts & Telecom Press, Beijing, 2017.
[18] Sue H L, Chee S C, Simon J M, Paolo R. How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 2017; 71: 1–13.
[19] Andrew G H, Zhu M L, Chen B, Dmitry K, Wang W J, Tobias W, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017.
[20] Dmytro M, Nikolay S, Jiri M. Systematic evaluation of CNN advances on the imagenet. Computer Vision & Image Understanding, 2016.
[21] Matthew D Z, Rob F. Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014; pp.818–833.
[22] Karen S, Andrew Z. Very deep convolutional networks for large-scale image recognition. ICLR, 2015.
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[24] Cotton spider mites. https://baike.baidu.com/item/%e6%a3% 89%e5%8f%b6%e8%9e%a8/5296015?Fr=aladdin.
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[33] CS231n: Convolutional neural networks for visual recognition. http://cs231n.stanford.ddu/.
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Published
2019-12-04
How to Cite
Yang, L., Luo, J., Wang, Z., Chen, Y., & Wu, C. (2019). Research on recognition for cotton spider mites’ damage level based on deep learning. International Journal of Agricultural and Biological Engineering, 12(6), 129–134. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4816
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Information Technology, Sensors and Control Systems
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