A UNIFIED MATRIX-BASED CONVOLUTIONAL NEURAL NETWORK FOR FINE-GRAINED IMAGE CLASSIFICATION OF WHEAT LEAF DISEASES

Abstract

Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit (ELU), local response normalization (LRN), etc. to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared to the commonly used plain networks. Therefore, it will create more non-linear mappings and enhance representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16,652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83,260 images. M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and testing accuracy of 90.1%. This outperformed AlexNet and VGG-16. M-bCNN demonstrated accuracy gains with convolutional kernel matrix in fine-grained image classification.

Existing System

Support Vector Machine (SVM), SVM-based Multiple Classifier System (MCS)

Proposed System

In an attempt to leverage the success of CNN for object classification, the M-bCNN for fine-grained image classification of wheat leaf disease is proposed. It describes the new hierarchical M-bCNN architecture that integrates the proposed convolutional kernel matrix and other tricks, such as ELU, LRN, and DropConnect. Convolutional kernel matrix aims at increasing the model’s representational ability so as to learn a domain-specific discrimination to deal with fine-grained classification, whilst suppressing parameter growth rate.  

Architecture

BLOCK DIAGRAM