基于改进区域卷积神经网络的田间玉米叶部病害识别
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新疆维吾尔自治区研究生科研创新项目(XJ2019G033);国家级大学生创新创业训练计划(201810755079S);新疆维吾尔自治区天山雪松科技创新领军人才计划(2018xs01)


Recognition of field maize leaf diseases based on improved regional convolutional neural network
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    目的 引入区域卷积神经网络Faster R-CNN算法并对其改进,以实现在田间真实环境下背景复杂且具有相似病斑特征的玉米病害的智能诊断。方法 在玉米田间和公开数据集网站获取具有复杂背景的9种常见病害图像1 150幅,人工标注后对原始图像进行离线数据增强扩充;对Faster R-CNN算法进行适应性改进,在卷积层加入批标准化处理层,引入中心代价函数构建混合代价函数,提高相似病斑的识别精度;采用随机梯度下降算法优化训练模型,分别选取4种预训练的卷积结构作为Faster R-CNN的特征提取网络进行训练,并测试得到最优特征提取网络,利用训练好的模型选取不同天气条件下的测试集进行对比,并将改进Faster R-CNN与未改进的Faster R-CNN和SSD算法进行对比试验。结果 在改进Faster R-CNN病害识别框架中,以VGG16卷积层结构作为特征提取网络具有更出色的性能,利用测试集图像检验模型,识别结果的平均精度为 0.971 8,平均召回率为0.971 9,F1为0.971 8,总体平均准确率可达97.23%;晴天的图像识别效果优于阴天的。改进Faster R-CNN算法与未改进的Faster R-CNN算法相比,平均精度高0.088 6,单张图像检测耗时减少0.139 s;与SSD算法相比,平均精度高0.0425,单张图像检测耗时减少0.018 s,表明在大田环境中具有复杂背景的玉米病害智能检测领域,改进Faster R-CNN算法综合性能优于未改进的Faster R-CNN算法和SSD算法。结论 将改进后的Faster R-CNN算法引入田间复杂条件下的玉米病害智能诊断是可行的,具有较高的准确率和较快的检测速度,能够避免传统人工识别的主观性,该方法为田间玉米病害的及时精准防控提供了依据。

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    Objective To realize intelligent diagnosis of maize leaf diseases with similar spots and complicated background in real field conditions by introducing and improving a regional convolutional neural network algorithm, Faster R-CNN.Method We obtained 1 150 maize leaf images with complicated background for nine kinds of common diseases from maize field and public dataset websites. After manual annotation of the original images, offline data augmentation was used to enlarge the image data. The Faster R-CNN algorithm was introduced and improved for adaptive application by adding batch normalization processing layer and introducing center cost function to improve the identification accuracy of similar disease spots. We used the stochastic gradient descent algorithm to train and optimize this model. Four pre-trained convolution structures for feature extraction were selected and compared in Faster R-CNN training and testing to get the most optimal model. During the test, the trained model was used to select test sets under different weather conditions for comparison, and improved Faster R-CNN was also compared with unimproved Faster R-CNN and SSD algorithm.Result In the framework of improved Faster R-CNN, VGG16 convolutional feature extraction network had better performance than others. The testing image data set was used to verify the model performance, and the average precision of final recognition result was 0.971 8, the average recall rate was 0.971 9, F1 was 0.971 8, and the overall average accuracy reached 97.23%. The recognition effect under sunny conditions was better than that of cloudy conditions. The average precision of improved Faster R-CNN increased by 0.088 6 and the detection time per image decreased by 0.139 s compared with unimproved Faster R-CNN algorithm. The average precision of proposed method was 0.0425 higher than that of SSD algorithm, and the detection time per image decreased by 0.018 s. The results indicated that the improved Faster R-CNN algorithm was superior to unimproved Faster R-CNN and SSD algorithm in the field of intelligent detection of maize diseases under complex field conditions.Conclusion It is feasible to introduce improved Faster R-CNN algorithm into the intelligent diagnosis of maize diseases under complex field conditions, and it has higher accuracy and faster detection speed, which can avoid the subjectivity of traditional artificial identification. The proposed method lays a foundation for precise prevention and control of maize disease in field environment.

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樊湘鹏,周建平,许燕.基于改进区域卷积神经网络的田间玉米叶部病害识别[J].华南农业大学学报,2020,41(6):82-91

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  • 收稿日期:2020-08-12
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  • 在线发布日期: 2020-11-17