查看更多>>摘要：? 2022 Elsevier B.V.The monitoring and prevention and control of citrus yellow dragon disease is a significant measure to ensure citrus production. If yellow dragon disease appears in citrus orchards, it will cause root rot, fruit deformation and wilting of fruit trees, which will eventually spread to every fruit tree in the whole orchard and cause the death of fruit trees, so it is very meaningful to detect the symptoms of citrus yellow dragon disease early and take appropriate treatment and prevention measures. Pratically, the orchard owner will remove the corresponding fruit trees as soon as they are found to be infected with Huanglong disease, so that it is extremely problematic to obtain a large number of Huanglong disease leaf data. Meanwhile, due to the uncertainty of the pathological trait distribution of citrus yellow dragon disease leaves and the extreme shortage of data, the convolutional neural network model learned in a small number of samples is not capable of generalization. In order to improve the accuracy and generalization of Citrus Greening Disease recognition algorithm, this paper introduces Texture Reconstruction Loss CycleGAN(TRL-GAN) to generate citrus diseased leaf data in realistic scene to increase the richness of samples, and thus proposes the Recognizing Citrus Greening Based on TRL-GAN(RCG TRL-GAN). This algorithm firstly performs background culling by using the instance segmentation network Mask RCNN for realistic scenes citrus yellow dragon disease mottled, zinc deficiency, magnesium deficiency, leaf veins yellowing and other corresponding symptomatic leaves, then introduces texture reconstruction loss improvement CycleGAN as training and migrates the diseased leaf style to ordinary green leaves for data expansion, and finally uses the expanded dataset to train the convolutional neural network. Experimental results on the constructed dataset of 4516 images (762 mottled, 749 Zn deficient, 737 Mg deficient, 721 Vein yellowing, 783 Diachyma yellowing, 764 green leaves) reveal that TRL-GAN has 13.49% and 1.1% improvement in FID and KID, respectively, relative to the original structure CycleGAN, and has been identified by six citrus yellow dragon disease experts and three vision professionals identify that the fake data generated by TRL-GAN have similarity with the leaf pathological characteristics and real data, and also by using T-SNE technique it is observed that the real data have similar distribution with the generated fake data in two-dimensional plane. Meanwhile, the more outstanding accuracy performance in the classification network is ResNeXt101 with 97.45% accuracy, and the average accuracy of RCG TRL-GAN technique in the recognition of classification network is improved 2.76%. The study proves that the RCG TRL-GAN effectively improves the citrus greening disease phenotype data generation and recognition, and can provide method reference for the expansion and recognition of complex plant disease phenotype images.
NSTL
Elsevier