| Tan Wenxue (谭文学),Zhao Chunjiang,Wu Huarui.[J].高技术通讯(英文),2016,22(1):67~74 |
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| CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors |
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| DOI：10.3772/j.issn.1006-6748.2016.01.010 |
| 中文关键词: |
| 英文关键词: lesion image, self-adaptive momentum(SM) convolutional neural network(CNN), deep learning, early warning, agri-sensor |
| 基金项目: |
| Author Name | Affiliation | | Tan Wenxue (谭文学) | | | Zhao Chunjiang | | | Wu Huarui | |
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| 中文摘要: |
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| 英文摘要: |
| Video sensors and agricultural IoT (internet of things) have been widely used in the informationalized orchards. In order to realize intelligent-unattended early warning for disease-pest, this paper presents convolutional neural network (CNN) early warning for apple skin lesion image, which is real-time acquired by infrared video sensor. More specifically, as to skin lesion image, a suite of processing methods is devised to simulate the disturbance of variable orientation and light condition which occurs in orchards. It designs a method to recognize apple pathologic images based on CNN, and formulates a self-adaptive momentum rule to update CNN parameters. For example, a series of experiments are carried out on the recognition of fruit lesion image of apple trees for early warning. The results demonstrate that compared with the shallow learning algorithms and other involved, well-known deep learning methods, the recognition accuracy of the proposal is up to 96.08%, with a fairly quick convergence, and it also presents satisfying smoothness and stableness after convergence. In addition, statistics on different benchmark datasets prove that it is fairly effective to other image patterns concerned. |
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