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Intelligent alerting for fruit-melon lesion image based on momentum deep learning

文献类型： 外文期刊

作者： Tan, Wenxue 1 ; Zhao, Chunjiang 3 ; Wu, Huarui 3 ;

作者机构： 1.Beijing Univ Technol, Coll Comp Sci, Beijing 100022, Peoples R China

2.Hunan Univ Arts & Sci, Changde 415000, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Lesion image;CNN;Deep network;Momentum learning;Intelligent alerting

期刊名称：MULTIMEDIA TOOLS AND APPLICATIONS （ 影响因子：2.757； 五年影响因子：2.517 ）

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收录情况： SCI

摘要： Sensors and Internet of things (IoT) have been widely used in the digitalized orchards. Traditional disease-pest recognition and early warning systems, which are based on knowledge rule, expose many defects, discommodities, and it is difficult to meet current production management requirements of the fresh planting environment. On purpose to realize an intelligent unattended alerting for disease-pest of fruit-melon, this paper presents the convolutional neural network (CNN) for recognition of fruit-melon skin lesion image which is real-timely acquired by an infrared video sensor, which network is grounded upon so-called momentum deep learning rule. More specifically, (1) a suite of transformation methods of apple skin lesion image is devised to simulate orientation and light disturbance which always occurs in orchards, then to output a self-contained set of almost all lesion images which might appear in various dynamic sensing environment; and (2) the rule of variable momentum learning is formulated to update the free parameters of CNN. Experimental results demonstrate that the proposed presents a satisfying accuracy and recall rate which are up to 97.5 %, 98.5 % respectively. As compared with some shallow learning algorithms and generally accepted deep learning ones, it also offers a gratifying smoothness, stableness after convergence and a quick converging speed. In addition, the statistics from experiments of different benchmark data-sets suggests it is very effective to recognize image pattern.

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