| 双通道卷积神经网络在南瓜病害识别上的应用 |
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| 引用本文： | 王昌龙,张远东,缪宏,杨煜恒.双通道卷积神经网络在南瓜病害识别上的应用[J].计算机工程与应用,2021,57(5):183-189. |
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| 作者姓名： | 王昌龙 张远东 缪宏 杨煜恒 |
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| 作者单位： | 1.扬州大学 机械工程学院，江苏 扬州 225127
2.东南大学 软件学院，江苏 苏州 215000 |
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| 基金项目： | 江苏省科技计划项目;扬州市科技计划项目;江苏省农机三新工程项目 |
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| 摘 要： | 针对南瓜病害识别工作量大、病害甄别难度高和农药利用率低等问题，提出一种基于K-means聚类与LBP特征的南瓜叶片病害识别方法，为智能机器人精准施药作业提供理论依据与技术支撑。该方法基于K-means聚类分割南瓜叶片病斑并经过形态学处理去除噪声，然后标定病斑采样区计算病斑LBP特征图，最终经由双通道特征提取网络及特征融合网络完成对病斑全局特征与细节特征的提取并使用Softmax分类器进行南瓜叶片病害识别。实验结果显示，提出的南瓜叶片病害识别方法能够以较高的准确率识别叶斑病、白粉病及霜霉病，性能优于采用病斑灰度图和LBP特征图的单通道CNN病害识别方法，满足施药机器人精准施药作业要求，利于南瓜病害防治工作。
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| 关 键 词： | 南瓜病害 施药机器人 K-means聚类 局部二值模式（LBP）特征 卷积神经网络 |
Application of Double Channel Convolutional Neural Network in Pumpkin Diseases Identification |
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| WANG Changlong,ZHANG Yuandong,MIAO Hong,YANG Yuheng.Application of Double Channel Convolutional Neural Network in Pumpkin Diseases Identification[J].Computer Engineering and Applications,2021,57(5):183-189. |
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| Authors: | WANG Changlong ZHANG Yuandong MIAO Hong YANG Yuheng |
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| Affiliation: | 1.College of Mechanical Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
2.College of Software Engineering, Southeast University, Suzhou, Jiangsu 215000, China |
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| Abstract: | Aiming at the problems of large workload of pumpkin diseases identification, high difficulty in diseases identification and low pesticide utilization rate, this paper proposes an identification method of pumpkin leaf diseases based on K-means clustering and LBP feature, which provides theoretical basis and technical support for precision application of intelligent robots. This method segments pumpkin leaf diseases spots based on K-means clustering and removes noise through morphological treatment. Then, diseases spots sampling area is demarcated and LBP operator is used to extract the feature of diseases spots. Finally, gray-scale map and LBP feature map of sampling area are input into two-channel convolutional neural network for feature extraction, and weighted fusion classification network is used for feature fusion and Softmax is used for pumpkin leaf diseases identification. The experimental results show that the pumpkin leaf disease identification method proposed in this paper can identify leaf spot, powdery mildew and downy mildew with high accuracy, and its performance is better than the single-channel CNN disease identification method using gray-scale map of leaves, which meets the precision application requirements of spraying pesticide robots and means that this method is beneficial to the control of pumpkin diseases. |
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| Keywords: | pumpkin diseases spraying pesticide robots K-means clustering Local Binary Pattern（LBP） feature convolutional neural network |
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