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- 저자 : Wenfeng Cong(Wenfeng Cong) (검색결과 3 건)
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Wenjie Tong(Wenjie Tong),Junying Li(Junying Li),Wenfeng Cong(Wenfeng Cong),Cuiping Zhang(Cuiping Zhang),Zhaoli Xu(Zhaoli Xu),Xiaolong Chen(Xiaolong Chen),Min Yang(Min Yang),Jiani Liu(Jiani Liu),Lei Yu 한국식물병리학회 2022 Plant Pathology Journal Vol.38 No.6
Root-knot nematode disease is a widespread and catastrophic disease of tobacco. However, little is known about the relationship between rhizosphere bacterial community and root-knot nematode disease. This study used 16S rRNA gene sequencing and PICRUSt to assess bacterial community structure and function changes in rhizosphere soil from Meloidogyne incognita-infected tobacco plants. We studied the rhizosphere bacterial community structure of M. incognita-infected and uninfected tobacco plants through a paired comparison design in two regions of tobacco planting area, Yuxi and Jiuxiang of Yunnan Province, southwest China. According to the findings, M. incognita infection can alter the bacterial population in the soil. Uninfested soil has more operational taxonomic unit numbers and richness than infested soil. Principal Coordinate Analysis revealed clear separations between bacterial communities from infested and uninfested soil, indicating that different infection conditions resulted in significantly different bacterial community structures in soils. Firmicutes was prevalent in infested soil, but Chloroflexi and Acidobacteria were prevalent in uninfested soil. Sphingomonas, Streptomyces, and Bradyrhizobium were the dominant bacteria genera, and their abundance were higher in infested soil. By PICRUSt analysis, some metabolism-related functions and signal transduction functions of the rhizosphere bacterial community in the M. incognita infection-tobacco plants had a higher relative abundance than those uninfected. As a result, rhizosphere soils from tobacco plants infected with M. incognita showed considerable bacterial community structure and function alterations.
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Effect Factor Analysis of Spraying Quality for Agricultural Chemicals
Wenfeng Sun,Qichao Li,Yongcun Fan,Yanhua Wan,Teng Wang,Baozhong Cong 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.11
As one of the most important parts of the agricultural work, agricultural chemicals spray is to protect the plants from weeds, pest and bacteria. With development of high efficient agricultural chemicals, the consumption of agricultural chemicals should be controlled within a couple of liter per hectare in theory. But it’s very hard to realize effective use of agricultural chemicals in fact. In this paper, the most effect factors of spray quality such as droplets size, environment temperature, droplets density, nozzle type and spray method were analyzed in agricultural chemicals spray operation. Droplet size effects drift and uniformity of droplet distribution directly. Environment temperature effects drift and uniformity of droplet distribution greatly. Character of nozzle affects spraying quality directly. All research of the paper can offer valuable reference to appropriate application of agricultural chemicals.
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Efficient Visual Place Recognition by Adaptive CNN Landmark Matching
( Yutian Chen ),( Wenyan Gan ),( Yi Zhu ),( Hui Tian ),( Cong Wang ),( Wenfeng Ma ),( Yunbo Li ),( Dong Wang ),( Jixian He ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.11
Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.
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