期刊
ADVANCES IN MULTIMEDIA
卷 2018, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2018/6710865
关键词
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资金
- National Science and technology support program [2014BAD12B01-1-3]
- Public Welfare Industry (Agriculture) Research Projects Level-2 [201503116-04-06]
- Postdoctoral Foundation of Heilongjiang Province [LBHZ15020]
- Harbin Applied Technology Research and Development Program [2017RAQXJ096]
- Economic Decision Making and Early Warning of Soybean Industry in Technology Collaborative Innovation System of Soybean Industry in Heilongjiang Province [20170401]
This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD) the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as fc). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.
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