Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
Source abstract
Abstract
Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and Land Use · Smart Agriculture and AI · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
School of Information Engineering, Yulin University, Yulin 719000, China
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
Shaanxi Engineering Research Center of Agricultural Information Intelligent Perception and Analysis, Xianyang 712100, China
Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China
Funding
National Natural Science Foundation of China · Shaanxi’s Key Research and Development Program · Key Industry Chain of Shaanxi Provincial Key Research and Development Program · Xianyang’s Key Research and Development Program · Technology Innovation Leading Program of Shaanxi · Key technology projects of key agricultural industry chain in Xi’an · National Natural Science Foundation Youth Project
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