A Crop Leaf Disease Image Recognition Method Based on Bilinear Residual Networks

Yun He, Quan Gao, Zifei Ma and Jude Hemanth

Mathematical Problems in Engineering, 2022, vol. 2022, 1-15

Abstract: Deep learning models are widely used in crop leaf disease image recognition. These models can be divided into two categories: global model and local model. The global model directly takes the whole leaf disease images as input to training and recognition. It can achieve end-to-end training and recognition, which is very convenient to use. But this kind of model cannot very accurately and completely extract the features from the very small diseased spots in the image. Before training and recognizing, the local model needs to extract the diseased spots part from the image by image segmentation technology. Then the local model takes the disease spots part images as input to training and recognition. Features extracted by local model are more accurate and complete. But this kind of model cannot achieve end-to-end training and recognition, and the image segmentation will bring additional overhead. Considering the disadvantage of global model and local model, we proposed a crop leaf disease image recognition method based on bilinear residual networks (named DIR-BiRN). DIR-BiRN extracts features by two residual networks feature extractors and then integrates the features by a bilinear pooling function. By this way, it can extract features more accurately and completely while achieving end-to-end training and recognition. Experiments on the PlantVillage dataset show that, when compared with the standard ResNet-18 model, the DIR-BiRN improves on accuracy performance, recall performance, precision performance, and F1-measure performance by averages of 0.2918, 0.81641, 0.59185, and 0.52151 percentage points, respectively.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/2948506.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/2948506.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2948506

DOI: 10.1155/2022/2948506

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().