基于深度学习与高光谱成像的蓝莓果蝇虫害无损检测

针对蓝莓果蝇虫害分类识别存在效率低、准确度差等问题,采用深度学习方法对采集的蓝莓高光谱图像进行数据处理与分析,以实现蓝莓果蝇虫害的无损检测。首先蓝莓高光谱图像采用PCA进行降维,优选数据集PC2与PC3并进行拼接得到最佳数据集PC23,对数据集中图像进行旋转90°、旋转180°、模糊、高亮、低亮、镜像和高斯噪声共7种增强操作,使各数据集容量扩增为原始容量的18倍。然后采用VGG16、InceptionV3与ResNet50深度学习模型对蓝莓果蝇虫害图像进行检测,均取得了较高的识别准确率。其中ResNet50模型效率最高,且ResNet50模型的准确率最高,达到92.92%,损失率最低,仅有3.08%,因此ResNet50模型在蓝莓果蝇虫害无损检测方面整体识别效果最佳。为了进一步提高蓝莓果蝇虫害无损检测性能,从ECA注意力模块、Focal Loss损失函数与Mish激活函数3方面对ResNet50模型进行了改进,构建了改进的im-ResNet50模型。得出im-ResNet50模型识别准确率达95.69%,损失率为1.52%。试验结果表明, im-ResNet50模型有效提升了蓝莓果蝇虫害识别能力。采用Grad-CAM分析了im-ResNet50模型可解释性,能够快速、准确地无损检测蓝莓果蝇虫害。

Nondestructive Detection of Blueberry Fruit Fly Pests Based on Deep Learning and Hyperspectral Imaging

Aiming at the problems of low efficiency and poor accuracy in the classification and recognition of blueberry fruit fly pests, a deep learning method was proposed to process and analyze the collected blueberry hyperspectral images, so as to realize the nondestructive detection of blueberry fruit fly pests. Firstly, the dimension of blueberry hyperspectral image was reduced by PCA. And the better data set PC2 and PC3 was selected. The best data set PC23 was obtained by splicing PC2 and PC3. The seven enhancement operations were performed on the images in the dataset, including 90° rotation, 180° rotation, blur, brightness adjustment, mirror image and Gaussian noise, so as to expand the capacity of each data set to 18 times of the original capacity. Then the three deep learning models of VGG16, InceptionV3 and ResNet50 were used to recognize and detect blueberry fruit fly pest images, and high recognition accuracy was achieved. Among them, ResNet50 model had the highest efficiency, and the accuracy of ResNet50 model was the highest, reaching 92.92%, and the loss rate was the lowest, only 3.08%. Therefore, ResNet50 model had the best overall recognition effect on the nondestructive detection of blueberry fruit fly pests. Finally, an improved im-ResNet50 model was constructed based on ResNet50 model from three aspects: ECA attention module, Focal Loss loss function and Mish activation function. The recognition accuracy of im-ResNet50 model was 95.69%, and the loss rate was reduced to 1.52%. The results showed that im-ResNet50 model effectively improved the pest identification ability of blueberry fruit fly. The interpretability of im-ResNet50 model was also analyzed by Grad-CAM. The research results can quickly and accurately detect the blueberry fruit fly pests, and it can provide theoretical support for the intelligent detection and online sorting of small berry quality.