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

Remote Sensing

Xiangyu Lu¹  Jun Zhou¹  Jie Jiao¹  Jing Huang¹  Fei Liu¹  Yufei Liu¹  Rui Yang¹  Baofeng Su²  Peiwen Gu³ 

[1] College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China;School of Agriculture, Ningxia University, Yinchuan 750021, China;

关键词: grape foliage;    disease and pest;    detection;    multi-source data fusion;    ShuffleNet V2;   

DOI  :  10.3390/rs13245102

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown