Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 176, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105652
Keywords
Agriculture; Cucumber; Disease; Classification; Complex environment
Funding
- National Key R&D Program Advancing digital precision aquaculture in China (ADPAC) [2017YFE0122100]
- Institute of fishery machinery and instruments, Chinese Academy of Fishery Sciences Program of China Research of Intelligent Model and Precision Control Key Technologies in Facilities Aquaculture [2017YFD0701702]
- Guolian Aquatic Products Development Co. LTD Program Big data analysis and management cloud service platform construction and large-scale application of shrimp [2017B010126001]
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The intelligent identification and classification of greenhouse plant diseases is an important research object in smart horticulture. In this study, our main task is to find an efficient method to solve the problem of disease similarity caused by two kinds of diseases occurring in the same leaf and the influence of external light. First, we obtain a cucumber leaf disease dataset in a naturally complex greenhouse background, which includes not only powdery mildew, downy mildew, healthy leaves, but also the combination of powdery mildew and downy mildew. Secondly, we use the current state-of-the-art method EfficientNet to construct a classification model for the above four types, Model accuracy is 97%, and prove that EfficientNet-B4 is the most suitable method for this study. Finally, we constructed a two-classification model of cucumber similar diseases by using EfficientNet-B4 improved with the most state-of-the-art optimizer Ranger, obtained unexpected accuracy (96%). The experimental results show that our improved method has significant effect on the classification of similar diseases of cucumber.
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