摘要
收集咖啡和柑橘病虫害样本图片,利用TensorFlow深度学习框架,在原始SRGAN(Super-resolution generative adversarial networks)的超分辨率重建网络里加入了注意力模块,对重建图像视觉质量和峰值信噪比(PSNR)、结构化相似性(SSIM)指标进行分析。结果表明,设计的模型和原始SRGAN模型对比之后峰值信噪比提高了2.23,结构相似性提高了7%。在细节纹理方面可以获得更好的视觉效果,重建后的图像识别准确率提高了约4.42个百分点。因此,设计的模型可以对小样本性质的植物病虫害样本进行扩充。
The sample pictures of coffee and citrus pests and diseases were collected,and an attention module was added to the super-resolution reconstruction network of the original SRGAN by using TensorFlow deep learning framework.The visual quality,peak sig-nal-to-noise ratio and structured similarity index of the reconstructed image were analyzed.The results showed that the peak signal-to-noise ratio of the designed model was improved by 2.23,and the structural similarity was enhanced by 7%,after comparing with the original SRGAN mode.Better visuals could be obtained in terms of detail texture,and the accuracy of the reconstructed image clas-sification was improved by about 4.42 percentage points.Therefore,the model designed could be used for the expansion of samples of plant pests and diseases with small sample properties.
作者
费加杰
杨毅
曾晏林
蔺瑶
贺壹婷
黎强
张圣笛
FEI Jia-jie;YANG Yi;ZENG Yan-lin;LIN Yao;HE Yi-ting;LI Qiang;ZHANG Sheng-di(School of Big Data,Yunnan Agricultural University,Kunming 650500,China)
出处
《湖北农业科学》
2024年第9期204-209,共6页
Hubei Agricultural Sciences
基金
云南省重大科技专项(A3032021043002)。
