A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2

Disease and pest detection of grape foliage is essential for grape yield and quality. RGB image (RGBI), multispectral image (MSI), and thermal infrared image (TIRI) are widely used in the health detection of plants. In this study, we collected three types of grape foliage images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, and healthy) in the field. ShuffleNet V2 was used to build up detection models. According to the accuracy of RGBI, MSI, TIRI, and multi-source data concatenation (MDC) models, and a multi-source data fusion (MDF) decision-making method was proposed for improving the detection performance for grape foliage, aiming to enhance the decision-making for RGBI of grape foliage by fusing the MSI and TIRI. The results showed that 40% of the incorrect detection outputs were rectified using the MDF decision-making method. The overall accuracy of MDF model was 96.05%, which had improvements of 2.64%, 13.65%, and 27.79%, compared with the RGBI, MSI, and TIRI models using label smoothing, respectively. In addition, the MDF model was based on the lightweight network with 3.785 M total parameters and 0.362 G multiply-accumulate operations, which could be highly portable and easy to be applied.

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Multi-source data fusion and modeling to assess and communicate complex flood dynamics to support decision-making for downstream areas of dams: The 2011 hurricane irene and schoharie creek floods, NY

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2017.06.002 ◽

2017 ◽

Vol 62 ◽

pp. 157-173 ◽

Cited By ~ 3

Author(s):

Chris S. Renschler ◽

Zhihao Wang

Keyword(s):

Decision Making ◽

Data Fusion ◽

Hurricane Irene ◽

Source Data ◽

Flood Dynamics ◽

Support Decision Making

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A Method for Multiple Attribute Decision Making Based on the Fusion of Multisource Information

Abstract and Applied Analysis ◽

10.1155/2014/972159 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6

Author(s):

F. W. Zhang ◽

S. H. Xu ◽

B. J. Wang ◽

Z. J. Wu

Keyword(s):

Decision Making ◽

Data Fusion ◽

Traffic Engineering ◽

Multiple Attribute Decision Making ◽

Multiple Source ◽

Source Information ◽

Fusion Technique ◽

Multiple Attribute ◽

Decision Making Problem ◽

Source Data

We propose a new method for the multiple attribute decision making problem. In this problem, the decision making information assembles multiple source data. Two main advantages of this proposed approach are that (i) it provides a data fusion technique, which can efficiently deal with the multisource decision making information; (ii) it can produce the degree of credibility of the entire decision making. The proposed method performs very well especially for the scenario that there exists conflict among the multiple source information. Finally, a traffic engineering example is given to illustrate the effect of our method.

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