Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

Peng Wang; Tong Niu; Yanru Mao; Zhao Zhang; Bin Liu; Dongjian He

doi:10.3389/fpls.2021.723294

Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

Front Plant Sci. 2021 Sep 28:12:723294. doi: 10.3389/fpls.2021.723294. eCollection 2021.

Authors

Peng Wang^{1

2

3}, Tong Niu^{1

2

3}, Yanru Mao^{1

2

3}, Zhao Zhang^{1

2

3}, Bin Liu^{2

3

4}, Dongjian He^{1

2

3}

Affiliations

¹ College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China.
² Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang, China.
³ Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Xianyang, China.
⁴ College of Information Engineering, Northwest A&F University, Xianyang, China.

Abstract

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

Keywords: CA block; CA-ENet; apple disease; attention mechanism; diseases identification.