of leaves. Generally speaking, the deeper the network, the more the parameters and the
larger models. It spends a long time to classify objects. In order to solve these problems, this paper proposes an effective way to classify plant diseases by using binarized
convolutional neural networks. It aims to speed up the operation by binarizing the
parameters.
2 Related Work
Kan et al. [3] extracted the contours and texture features of the leaves by radial basis
function (RBF) neural network. The average recognition rate is 83.3%. Tan et al. [4]
can effectively identify lesion area of soybean by calculating the color value of leaves
and creating a multilayered BP neural network. The average recognition accuracy can
reach 92.1%. Zhang et al. [5] used the cloning algorithm and K-nearest neighbor
algorithm to classify leaves, which achieved a recognition rate of 91.37%. Wang et al.
[6] used the support vector machine (SVM) to identify the leaves; the accuracy of the
classifier can reach 91.41%.
Dyrmann et al. [7] used convolutional neural networks to classify plant images
taken with mobile phones, and the average recognition accuracy reached 86.2%.
Mohanty et al. [8] carried out experiments on 26 diseases of 14 kinds of plant; they
choose two models, three datasets, and five datasets with different proportions, which
also achieved good results. Lee et al. [9] explored how the CNN extracts features of
leaves. They tested different methods and contrasted various experimental results. The
results show that the CNN has a better effect on classification.
3 Dataset
This paper obtained 54,306 leaf images from PlantVillage by color, grayscale, and
segmentation, which contains a total of 38 plant types. According to the number of
different types of leaves in the dataset, the number of different kinds of leaf images
ranges from 64 to 1166. In order to make up for the shortcomings, this paper expands
the dataset by enhancing the exposure of the blade, changing the color of the image,
and rotating the image. Rotating the image is to eliminate the effects of inherent bias
[10]. Then, the images are labeled by category, and center cropped to a size of
224 Â 224.
4 Convolutional Neural Networks
Generally speaking, to classify two objects there are two steps: data forward transmission and weight updating. When starting to train a model, there are two choices: one
is to completely reset the parameters and train from scratch; the other is model finetuning. And the latter was used in this paper. Generally, the basic structure of CNN
includes a feature extraction layer and a feature mapping layer. The former includes a
convolution layer and a pooling layer, where each neuron is connected to the
Plant Diseases Identification Based on Binarized Neural Network
13
larger models. It spends a long time to classify objects. In order to solve these problems, this paper proposes an effective way to classify plant diseases by using binarized
convolutional neural networks. It aims to speed up the operation by binarizing the
parameters.
2 Related Work
Kan et al. [3] extracted the contours and texture features of the leaves by radial basis
function (RBF) neural network. The average recognition rate is 83.3%. Tan et al. [4]
can effectively identify lesion area of soybean by calculating the color value of leaves
and creating a multilayered BP neural network. The average recognition accuracy can
reach 92.1%. Zhang et al. [5] used the cloning algorithm and K-nearest neighbor
algorithm to classify leaves, which achieved a recognition rate of 91.37%. Wang et al.
[6] used the support vector machine (SVM) to identify the leaves; the accuracy of the
classifier can reach 91.41%.
Dyrmann et al. [7] used convolutional neural networks to classify plant images
taken with mobile phones, and the average recognition accuracy reached 86.2%.
Mohanty et al. [8] carried out experiments on 26 diseases of 14 kinds of plant; they
choose two models, three datasets, and five datasets with different proportions, which
also achieved good results. Lee et al. [9] explored how the CNN extracts features of
leaves. They tested different methods and contrasted various experimental results. The
results show that the CNN has a better effect on classification.
3 Dataset
This paper obtained 54,306 leaf images from PlantVillage by color, grayscale, and
segmentation, which contains a total of 38 plant types. According to the number of
different types of leaves in the dataset, the number of different kinds of leaf images
ranges from 64 to 1166. In order to make up for the shortcomings, this paper expands
the dataset by enhancing the exposure of the blade, changing the color of the image,
and rotating the image. Rotating the image is to eliminate the effects of inherent bias
[10]. Then, the images are labeled by category, and center cropped to a size of
224 Â 224.
4 Convolutional Neural Networks
Generally speaking, to classify two objects there are two steps: data forward transmission and weight updating. When starting to train a model, there are two choices: one
is to completely reset the parameters and train from scratch; the other is model finetuning. And the latter was used in this paper. Generally, the basic structure of CNN
includes a feature extraction layer and a feature mapping layer. The former includes a
convolution layer and a pooling layer, where each neuron is connected to the
Plant Diseases Identification Based on Binarized Neural Network
13