Improvement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.

中文翻译：

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使用增强的数据增强模型和深度学习从低质量图像中识别木薯病害

智能农业中深度学习算法的改进对于支持植物病害的早期检测非常重要，从而提高作物产量。由于对专家知识和专业设备的要求，机器学习应用程序的数据采集是一项昂贵的任务。任何应用程序在现实世界中的可用性通常受到非熟练用户和用于获取图像以进行分类的设备的限制。我们的目标是使用用于神经网络训练的数据增强技术来提高深度学习模型对低质量测试图像的准确性。我们生成具有修改后的颜色值分布的合成图像，以扩展可训练的图像颜色空间并训练神经网络识别重要的基于颜色的特征，它们对低质量图像的缺陷不太敏感，例如受模糊或运动影响的图像。本文介绍了一种新颖的图像颜色直方图变换技术，用于生成合成图像以在图像分类任务中进行数据增强。该方法基于切比雪夫正交函数与图像颜色直方图的概率分布函数的卷积。为了验证我们提出的模型，我们使用了四种方法（分辨率下采样、高斯模糊、运动模糊和过度曝光）来降低木薯叶病数据集的图像质量。基于改进的 MobileNetV2 神经网络的结果表明，与基线网络相比，木薯叶病害识别准确率在较低质量的测试图像上有统计学意义的显着提高。