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The Diagnosis of Cucumber Disease Based on Image Recognition

机译：基于图像识别的黄瓜疾病诊断

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摘要

A new method of diagnosis cucumber leaf disease based on computer image recognition is studied to improve cucumber disease diagnosis accuracy and efficiency. At first, vector median filter was applied to remove noise of the acquired color images of cucumber disease leaf. Then texture color features of color image of cucumber disease spot on lcaf were extracted, and recognition method of SVM for diagnosis of cucumber disease was used. Experimental results indicate that SVM has xcellent learning and generalization ability in solving learning problem with small training set of sample, the diagnosis performance by SVM is recognized more correct and faster, which is better than that of neural networks. The liner kernel function is most suitable diagnosis for cucumber disease based on color texture through the comparison of different kernel functions.
机译：研究了一种基于计算机图像识别的黄瓜叶病诊断新方法，以提高黄瓜病害诊断的准确性和效率。首先，应用矢量中值滤波器去除黄瓜病害叶片彩色图像的噪声。然后提取lcaf上黄瓜病斑彩色图像的纹理色彩特征，并采用支持向量机进行黄瓜病害诊断。实验结果表明，支持向量机在小样本训练集的情况下具有很好的学习和泛化能力，能够很好地解决学习问题，并且可以更快，更准确地识别支持向量机的诊断性能，优于神经网络。通过比较不同核函数，基于颜色纹理的线性核函数最适合黄瓜疾病的诊断。

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