谭文学,赵春江,吴华瑞,高荣华.基于弹性动量深度学习神经网络的果体病理图像识别[J].农业机械学报,2015,46(1):20-25.
Tan Wenxue,Zhao Chunjiang,Wu Huarui,Gao Ronghua.A Deep Learning Network for Recognizing Fruit Pathologic Images Based on Flexible Momentum[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(1):20-25.
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基于弹性动量深度学习神经网络的果体病理图像识别   [下载全文]
A Deep Learning Network for Recognizing Fruit Pathologic Images Based on Flexible Momentum   [Download Pdf][in English]
投稿时间:2014-08-28  
DOI:10.6041/j.issn.1000-1298.2015.01.004
中文关键词:  果蔬病害 病理图像 深度学习神经网络 弹性动量 图像识别
基金项目:“十二五”国家科技支撑计划资助项目(2013BAD15B04)、国家自然科学基金资助项目(61102126)和湖南文理学院重点(建设)学科建设项目
作者单位
谭文学 北京工业大学
湖南文理学院 
赵春江 北京农业信息技术研究中心 
吴华瑞 北京农业信息技术研究中心 
高荣华 北京农业信息技术研究中心 
中文摘要:为了实时预警果蔬病害和辅助诊断果蔬疾病,实现无人值守的病虫害智能监控,设计了深度学习神经网络的果蔬果体病理图像识别方法,基于对网络误差的传播分析,提出弹性动量的参数学习方法,以苹果为例进行果体病理图像的识别试验。结果表明,该方法召回率为98.4%;同其他同源更新机制相比,弹性动量方案能显著改善学习网络的果蔬病害识别准确率;其收敛曲线平滑,5h时耗能实现收敛,对不同数据集也有良好泛化性能。
Tan Wenxue  Zhao Chunjiang  Wu Huarui  Gao Ronghua
Beijing University of Technology;Hunan University of Arts and Science,Beijing Research Center for Information Technology in Agriculture,Beijing Research Center for Information Technology in Agriculture and Beijing Research Center for Information Technology in Agriculture
Key Words:Plant disease and insect pest Pathological image Deep learning network Flexile momentum Image recognition
Abstract:Agricultural internet of things (IOT) and sensor technology has been widely used in the informationalized and mechanized orchard. The research aimed at both constructing an automatic assistant diagnosis and a real time alerting for plant disease and insect pest. The purpose also covered to realize an unmanned pest disease monitoring and to release some human interaction in making a diagnosis. A method for pathologic image recognition diagnosis based on deep learning neural network was designed and an innovative method for updating free parameters of the network was proposed on the basis of analyzing the error propagation of the network, so called the gradient descendent with flexible momentum. Then, computer recognizing pathologic images of fruit sphere was researched into systematically, where the apple was selected as a subject. Experiment result revealed the method manifested a recall rate at 98.4%. And in parallel with several well known updating schemes based momentum, the proposal was able to obviously improve the accuracy of learning network with a flatter converging curve, at a cost of short converging time. The test upon the several popular benchmark data sets also demonstrated it could perform an effective recognition on the image pattern.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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