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

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.

Key words: pumpkin diseases, spraying pesticide robots, K-means clustering, Local Binary Pattern（LBP） feature, convolutional neural network