Microbial fungicides can positively affect aubergine photosynthetic properties, soil enzyme activity and microbial community structure.

Background: This study examined the effects of microbial agents on the enzyme activity, microbial community construction and potential functions of inter-root soil of aubergine (Fragaria × ananassa Duch.). This study also sought to clarify the adaptability of inter-root microorganisms to environmental factors to provide a theoretical basis for the stability of the microbiology of inter-root soil of aubergine and for the ecological preservation of farmland soil. Methods: Eggplant inter-root soils treated with Bacillus subtilis (QZ_T1), Bacillus subtilis (QZ_T2), Bacillus amyloliquefaciens (QZ_T3), Verticillium thuringiensis (QZ_T4) and Verticillium purpureum (QZ_T5) were used to analyse the effects of different microbial agents on the inter-root soils of aubergine compared to the untreated control group (QZ_CK). The effects of different microbial agents on the characteristics and functions of inter-root soil microbial communities were analysed using 16S rRNA and ITS (internal transcribed spacer region) high-throughput sequencing techniques. Results: The bacterial diversity index and fungal diversity index of the aubergine inter-root soil increased significantly with the application of microbial fungicides; gas exchange parameters and soil enzyme activities also increased. The structural and functional composition of the bacterial and fungal communities in the aubergine inter-root soil changed after fungicide treatment compared to the control, with a decrease in the abundance of phytopathogenic fungi and an increase in the abundance of beneficial fungi in the soil. Enhancement of key community functions, reduction of pathogenic fungi, modulation of environmental factors and improved functional stability of microbial communities were important factors contributing to the microbial stability of fungicide-treated aubergine inter-root soils.

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification.

Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep learning neural networks, especially the Convolutional Neural Networks (CNNs). Most of the existing works focused on classifying very clear natural images, evidenced by the widely used image databases, such as Caltech-256, PASCAL VOCs, and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification and whether degradation removal helps CNN-based image classification. More specifically, we wonder whether image classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image classification performance. In this article, we empirically study those problems for nine kinds of degraded images-hazy images, motion-blurred images, fish-eye images, underwater images, low resolution images, salt-and-peppered images, images with white Gaussian noise, Gaussian-blurred images, and out-of-focus images. We expect this article can draw more interests from the community to study the classification of degraded images.

Degraded Image Semantic Segmentation with Dense-Gram Networks.

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based pre-processing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields stateof-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets.