Plant Diseases Identification Based
on Binarized Neural Network
Xiufu Pu
1 , Qian Ning
1,2(&)
, Yinjie Lei
1 , Bingcai Chen
3,4 ,
Tiantian Tang
2 , and Ruiheng Hu
2
1 College of Electronics and Information Engineering, Sichuan University,
Chengdu 610065, China
ningq@scu.edu.cn
2 College of Physics and Electronics Engineering, Xinjiang Normal University,
Urumqi 830054, China
3 College of Computer Science and Technology Engineering,
Dalian University of Technology, Dalian 116024, China
4 College of Computer Science and Technology Engineering,
Xinjiang Normal University, Urumqi 830054, China
Abstract. Although the use of the convolutional neural network (CNN) improved the accuracy of object recognition, it still had a long-running time. In
order to solve these problems, the training and testing datasets were split at four
different proportions to reduce the impact of inherent error. Using model finetuning, the model converged in a small number of iterations, and the average
recognition accuracy of BWN test can reach 96.8%. In the segmented dataset,
the recognition accuracy of the former was 4.7 percentage points higher than the
latter by comparing color dataset and grayscale dataset, which proved that a
certain amount of color features will have a positive impact on the model. The
segmented dataset was 0.9 percentage points higher than the color dataset; it
shows that the model focused more on features of contour and texture by
eliminating the background of images. The experiments showed that the binarized convolutional neural network can effectively improve recognition efficiency and accuracy compared with traditional methods.
Keywords: Agricultural diseases Á Binarized model Á Image classification
1 Introduction
Plant diseases are one of the three natural disasters in China. In China, more than
250 billion kg of grain, fruits, vegetables, oil, and cotton is lost every year.
Visual inspection is one of the main traditional methods for diagnosing plant
diseases. However, there are two problems: The judgments made by farmers based on
experience are not all correct and the situation of plants will be worse without timely
and effective treatment for diseases [1]. In order to realize the diagnosis of agricultural
diseases rapidly and accurately, researchers have explored methods for identifying
multiple plant diseases [2], using machine learning and image processing technology.
Convolutional neural network is good at extracting the features of contour and texture
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 12–19, 2020.
https://doi.org/10.1007/978-981-15-0187-6_2
on Binarized Neural Network
Xiufu Pu
1 , Qian Ning
1,2(&)
, Yinjie Lei
1 , Bingcai Chen
3,4 ,
Tiantian Tang
2 , and Ruiheng Hu
2
1 College of Electronics and Information Engineering, Sichuan University,
Chengdu 610065, China
ningq@scu.edu.cn
2 College of Physics and Electronics Engineering, Xinjiang Normal University,
Urumqi 830054, China
3 College of Computer Science and Technology Engineering,
Dalian University of Technology, Dalian 116024, China
4 College of Computer Science and Technology Engineering,
Xinjiang Normal University, Urumqi 830054, China
Abstract. Although the use of the convolutional neural network (CNN) improved the accuracy of object recognition, it still had a long-running time. In
order to solve these problems, the training and testing datasets were split at four
different proportions to reduce the impact of inherent error. Using model finetuning, the model converged in a small number of iterations, and the average
recognition accuracy of BWN test can reach 96.8%. In the segmented dataset,
the recognition accuracy of the former was 4.7 percentage points higher than the
latter by comparing color dataset and grayscale dataset, which proved that a
certain amount of color features will have a positive impact on the model. The
segmented dataset was 0.9 percentage points higher than the color dataset; it
shows that the model focused more on features of contour and texture by
eliminating the background of images. The experiments showed that the binarized convolutional neural network can effectively improve recognition efficiency and accuracy compared with traditional methods.
Keywords: Agricultural diseases Á Binarized model Á Image classification
1 Introduction
Plant diseases are one of the three natural disasters in China. In China, more than
250 billion kg of grain, fruits, vegetables, oil, and cotton is lost every year.
Visual inspection is one of the main traditional methods for diagnosing plant
diseases. However, there are two problems: The judgments made by farmers based on
experience are not all correct and the situation of plants will be worse without timely
and effective treatment for diseases [1]. In order to realize the diagnosis of agricultural
diseases rapidly and accurately, researchers have explored methods for identifying
multiple plant diseases [2], using machine learning and image processing technology.
Convolutional neural network is good at extracting the features of contour and texture
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 12–19, 2020.
https://doi.org/10.1007/978-981-15-0187-6_2