SHANG Zeng-qiang,YANG Dong-fu,MA Zhi-pu.Automatic Identification of Soybean Leaf Diseases Based on UAV Image and Deep Convolution Neural Network[J].Soybean Science,2021,40(05):662-668.[doi:10.11861/j.issn.1000-9841.2021.05.0662]
基于深度卷积神经网络的大豆叶片多种病害分类识别
- Title:
- Automatic Identification of Soybean Leaf Diseases Based on UAV Image and Deep Convolution Neural Network
- 文献标志码:
- A
- 摘要:
- \为实现复杂田间背景下快速高效分类识别多种大豆叶片病害图像，以准确性、训练时间和训练误差为深度学习模型性能指标，对比评估不同深度卷积神经网络模型。首先将无人机收集到的大豆叶片病害图像数据集按7∶3的比例分为训练集与测试集。为扩充数据图像，对训练集原图进行数据增强。基于不同权重的微调和迁移学习训练策略，采用Inception-v3、VGG-19、ResNet-50和Xception 4种深度神经网络模型对大豆叶片多种病害进行分类识别，并进行田间验证试验。结果表明，在FT 75%训练策略下的Inception-v3模型准确性最高，为99.04%；与其他模型相比，FT 100%和FT 75%训练策略下的深度学习模型显示出较高的准确性和较低的训练误差，但训练时间也更长。将训练好的Inception-v3 FT 75%模型用于计算机视觉系统的结果表明该系统可有效实现田间复杂背景下大豆叶片病害的智能识别。
- Abstract:
- In order to realize fast and efficient classification and recognition of soybean leaf disease images under complex field background，convolution neural network models with different depth were compared and evaluated with accuracy，training time and learning error as performance indexes of deep learning model.Firstly，the soybean leaf disease image data set collected by UAV was divided into training set and test set according to the ratio of 7∶3.In order to expand the data image，the original image of training set was enhanced.Four kinds of deep neural network models，including perception-v3，vgg-19，resnet-50 and xception，were used to test the models based on different weights of fine-tuning and transfer learning training strategies，and field validation experiments were carried out.The results showed that the accuracy of perception-v3 model was the highest (99.04%) under the FT 75% training strategy；Compared with other models，FT 100% and FT 75% deep learning models showed higher accuracy and lower learning error，but the training time was longer.The trained perception-v3 ft 75% model was applied to the computer vision system，and the results showed that the system could effectively realize the intelligent recognition of soybean leaf diseases under the complex field background.
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备注/Memo
收稿日期：2021-04-06