Abstract In the existing machine vision technology for tomato leaf disease recognition, due to interference from the external environment, it is easy to generate noise during image acquisition, and the characteristics of different diseases are similar, which makes image disease recognition difficult. Therefore, we propose a new framework for tomato leaf disease recognition. First, the image is denoised and enhanced by Binary Wavelet Transform combined with Retinex (BWTR), noise points and edge points are removed, and important texture information is retained. Then, the tomato leaves were separated from the background using KSW optimizatied by Artificial Bee Colony algorithm (ABCK). Finally, the Both-channel Residual Attention Network model (B-ARNet) was used to identify the pictures. The application results of 8616 images show that the overall detection accuracy is about 89%. Experiments show that the tomato leaf disease recognition method based on the combination of ABCK-BWTR and B-ARNet is effective.

中文翻译：

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基于ABCK-BWTR和B-ARNet组合的番茄叶片病害识别

摘要 在现有的番茄叶片病害识别机器视觉技术中，由于受到外界环境的干扰，图像采集过程中容易产生噪声，且不同病害的特征相似，给图像病害识别带来困难。因此，我们提出了一个新的番茄叶病识别框架。首先，通过结合Retinex（BWTR）的二值小波变换对图像进行去噪和增强，去除噪声点和边缘点，保留重要的纹理信息。然后，使用通过人工蜂群算法（ABCK）优化的 KSW 将番茄叶与背景分离。最后，使用双通道残差注意力网络模型（B-ARNet）对图片进行识别。8616幅图像的应用结果表明，整体检测准确率约为89%。实验表明，基于ABCK-BWTR和B-ARNet相结合的番茄叶片病害识别方法是有效的。