| 基于卷积神经网络结合图像处理技术的荞麦病害识别 |
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| 引用本文： | 陈善雄,伍胜,于显平,易泽林,雷兴华.基于卷积神经网络结合图像处理技术的荞麦病害识别[J].农业工程学报,2021,37(3):155-163. |
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| 作者姓名： | 陈善雄 伍胜 于显平 易泽林 雷兴华 |
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| 作者单位： | 1. 西南大学计算机与信息科学学院，重庆 400715;;2. 西南大学农学与生物科技学院，重庆 400715;;3. 重庆市农业学校，重庆 401329; |
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| 基金项目： | 国家自然科学基金（41271292）；重庆市自然科学基金（cstc2019jcyj-msxmX0130） |
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| 摘 要： | 荞麦病害的发生极大地影响了荞麦的品质和产量,对病害的监测是确保荞麦产业健康发展的重要措施。该研究利用深度学习中卷积神经网络的多层特征提取方式,对荞麦病害的特征进行抽取,然后根据特征进行分类,最终实现对荞麦病害的判别。首先采用一种最大稳定极值区域(MSER,Maximally Stable Extremal Regions)和卷积神经网络(CNN,ConvolutionalNeuralNetwork)结合的方法对荞麦发病区域进行检测,实现了病害区域与非病害区域的分离,准确定位病灶位置;然后在传统卷积神经网络框架上,通过提升网络宽度,约束参数量,加入了两级inception结构,对成像环境复杂,低质量荞麦图像准确地进行特征抽取。同时,为了降低采样过程中光照的影响,采用基于余弦相似度的卷积代替传统的卷积运算,对于光照不均的荞麦叶片也能够进行较好的病害识别。最后,为了验证该研究所提方法的有效性,建立一个包含8种荞麦病害图像的数据集,结果表明采用MSER和CNN结合的区域检测与两级inception识别框架的方法,对于荞麦是否发病判别的精确率、召回率、以及精确率和召回率加权调和平均值分别达到了97.54%,96.38%,97.82%;对于具体病害的识别其均值为84.86%,85.78%,85.40%。该方法在识别精度和速度方面具有良好的性能,为实现荞麦病害的自动识别提供了重要的技术支持。
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| 关 键 词： | 图像处理 病害 图像识别 特征提取 荞麦 卷积神经网络 深度学习 |
| 收稿时间： | 2020/11/15 0:00:00 |
| 修稿时间： | 2020/12/31 0:00:00 |
Buckwheat disease recognition using convolution neural network combined with image processing |
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| Chen Shanxiong,Wu Sheng,Yu Xianping,Yi Zelin,Lei Xinghua.Buckwheat disease recognition using convolution neural network combined with image processing[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(3):155-163. |
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| Authors: | Chen Shanxiong Wu Sheng Yu Xianping Yi Zelin Lei Xinghua |
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| Institution: | 1. College of Computer and Information Science, Southwest University, Chongqing 400715, China;;2. School of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China;; 3. Chongqing Municipal Agricultural School, Chongqing, 401329, China; |
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| Abstract: | Buckwheat is widely cultivated in the regions of high altitude and cold mountains, such as northern and southwest China. The occurrence of crop diseases has posed a great threat to the quality and yield of buckwheat. Disease surveillance is an important measure to ensure the healthy development of the buckwheat industry. Since artificial intelligence has been extending to precision agriculture in recent years, machine learning and pattern recognition are beneficial to image classification, detection, and recognition for high accuracy and efficiency, while reducing the overhead in the detection of crop diseases. However, the existing deep learning cannot consider the complexity of disease images collected in the field, such as leaf overlap, uneven lighting, and shadow coverage. Therefore, it is necessary to accurately extract key features in a complex imaging environment for accurate recognition of crop diseases. In this study, a feasible multi-layer feature extraction in deep learning was proposed to extract the features of buckwheat diseases. A surveillance system of disease was then established according to the classified characteristics, thereby automatically identifying the categories of buckwheat diseases. Firstly, a combination of maximally stable extremal regions (MSER) and convolutional neural network (CNN) was used to detect the feature regions of buckwheat disease, where the disease and disease-free areas were separated to precisely locate the disease position. A two-level inception structure was then added to the traditional CNN. The first layer was used to extract the contour features of the disease, where the dimension of the feature was represented by shallow information. The second layer was selected to obtain more detailed features, so that the number of parameters was relatively small in each layer, avoiding the gradient disappearance in the training process. This structure was normally used to extract the features for the low-quality images of buckwheat disease, particularly for the higher accuracy of classification. An improved convolution based on cosine similarity was utilized rather than the traditional convolution operation, in order to reduce the sensitivity of illumination during the sampling process. After that, the positions with similar features to the convolution kernel behaved higher activation values in the feature map. In addition, the difference between features was also reduced to prevent the interference of sample noise, thereby achieving better feature extraction for samples with uneven illumination. Finally, a systematic dataset was established, including some images for eight types of buckwheat diseases. The results showed that the combined framework of MSER and CNN with the two-level inception recognition was an effective way for region detection. In the identification for the presence or absence of disease, the accuracy, recall, and F1-measure reached 97.54%, 96.38%, and 97.82%, respectively. Furthermore, the mean for the recognition of disease categories reached 84.86%, 85.78%, and 85.4%, respectively, indicating excellent performance in recognition accuracy and speed. The finding can provide promising technical support for the automatic recognition of buckwheat diseases. |
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| Keywords: | image processing diseases image recognition feature extraction buckwheat convolution neural network deep learning |
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