Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network. (23rd February 2021)

• Record Type: Journal Article
• Authors: Ma, Li; Guo, Xueliang; Zhao, Shuke; Yin, Doudou; Fu, Yiyi; Duan, Peiqi; Wang, Bingbing; Zhang, Li
• Other Names: Wang Wei Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-02-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6683255
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.