用遗传算法训练的人工神经网络识别番茄生理病害果

    Automated identification of tomatoes with diseases using artificial neural network trained with genetic algorithms

    • 摘要: 综合运用计算机视觉技术、遗传算法、人工神经网络技术,实现番茄生理病害果的自动识别。首先,通过计算机视觉系统获取番茄的图像,利用图像的圆度值判别空洞果,利用图像的果径变化特征判别变形果。其次,采用遗传算法训练的人工神经网络进行试验研究。试验表明,该方法能准确识别番茄的形状,满足分级的要求,对番茄生理病害果的识别准确率可以达到100%。

       

      Abstract: Computer vision, genetic algorithm and artificial neural network technology were synthetically applied to automatically identify the tomatoes with diseases. First, the images of tomato were captured through computer vision system, then to identify empty tomatoes applying the round value and detect abnormal tomatoes applying the variation of fruit diameter. Second, artificial neural network trained with genetic algorithms was employed to conduct experimental research. The experiments show that the methods mentioned can accurately identify tomatoes shape and meet the requirement of the classification. The accuracy rate of the identification of tomatoes with diseases is up to 100%.

       

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