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

Rui Yang,Peiwen Gu,Jing Huang,Yufei Liu,Jie Jiao,Jun Zhou,Fei Liu,Baofeng Su,Xiangyu Lu

doi:10.3390/rs13245102

Abstract

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.

Highlights

During the growth of grape plants, it is difficult to avoid disease infection and pest damage
1x, multi-source data concatenation (MDC), multispectral image (MSI), RGB image (RGBI), and thermal infrared image (TIRI) models are trained on the relevant sets
With the lightweight network and multi-source data collected in the field, we provided an effective method for multi-source data fusion (MDF) decision-making and proposed an improved model with higher precision and good practical application potential, which could be a solution for the disease and pest detection of grape foliage in complicated environments

Summary

Introduction

During the growth of grape plants, it is difficult to avoid disease infection and pest damage. Common grape diseases and pests include anthracnose (a fungal disease), downy mildew (a fungal disease), viral disease (usually caused by a variety of viruses, such as grapevine leafrool-associated virus, grapevine leafrool-associated virus, grapevine virus A, and grapevine Pinot gris virus.), mites (pests), and leafhopper (pests), etc. They can damage the root, branch, foliage, and fruit of grape plants, deprive the photoassimilates, and interfere with the photosynthesis and nutrient absorption of grape plants [3]. Infected grape plants often develop symptoms on the foliage, such as obvious changes in leaf color, texture, and shape [4]

Objectives

Methods

Results

Discussion

Conclusion