Guangning Shen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Guangning Shen has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Chao Chen
School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
1 shared publication - Jihan Tao
Shandong Institute of Pomology, 66 Longtan Road, Tai’an Shandong, 271000, China
1 shared publication - Jinping Wang
Shandong Institute of Pomology, 66 Longtan Road, Tai’an Shandong, 271000, China
1 shared publication - Mingyue Yang
School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
1 shared publication - Naibin Duan
Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences, Shandong International Cooperation Laboratory for Agricultural Germplasm Resource Innovation , Jinan 250100
1 shared publication - Shiwei Dai
Shandong Agricultural Development Equipment Technology Co., Ltd., Taian 271600, China
1 shared publication
Latest Publications
- Open Access
- 12 November 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (22) , 2485
LCIC-YOLO: A Lightweight Rice Pest Detection Network with Local–Context Complementary Representation and Independent Cooperative Gating
Rice pest detection in natural field images is affected by weak visual cues and interference from complex crop backgrounds. To improve the use of local appearance and surrounding contextual information, this study proposes LCIC-YOLO, a lightweight detector based on YOLO11n. An ICGSimConv module is introduced into the high-resolution pathway to extract Local and Context features and regulate them independently using energy-based spatial responses before fusion. A six-class RP6 dataset containing 2637 images and 4312 annotated instances was curated from multiple public sources using duplicate detection and visual-family-based partitioning. Across three training seeds under the RP6 evaluation protocol, LCIC-YOLO achieved an mAP of 0.8826 ± 0.0051 and an mAP of 0.5902 ± 0.0047, with 2.60 M parameters and 6.57 GFLOPs. Its mean mAP exceeded YOLO11n by 2.67 percentage points. The control with matched gating and the fusion ablations support the contribution of Local information when integrated through branch-specific response estimation and independent Local–Context regulation. These results indicate that LCIC-YOLO improves the use of complementary spatial information while retaining the lightweight characteristics of the baseline detector.- 12 November 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (22) , 2485
Open AccessLCIC-YOLO: A Lightweight Rice Pest Detection Network with Local–Context Complementary Representation and Independent Cooperative Gating
Rice pest detection in natural field images is affected by weak visual cues and interference from complex crop backgrounds. To improve the use of local appearance and surrounding contextual information, this study proposes LCIC-YOLO, a lightweight detector based on YOLO11n. An ICGSimConv module is introduced into the high-resolution pathway to extract Local and Context features and regulate them independently using energy-based spatial responses before fusion. A six-class RP6 dataset containing 2637 images and 4312 annotated instances was curated from multiple public sources using duplicate detection and visual-family-based partitioning. Across three training seeds under the RP6 evaluation protocol, LCIC-YOLO achieved an mAP of 0.8826 ± 0.0051 and an mAP of 0.5902 ± 0.0047, with 2.60 M parameters and 6.57 GFLOPs. Its mean mAP exceeded YOLO11n by 2.67 percentage points. The control with matched gating and the fusion ablations support the contribution of Local information when integrated through branch-specific response estimation and independent Local–Context regulation. These results indicate that LCIC-YOLO improves the use of complementary spatial information while retaining the lightweight characteristics of the baseline detector. - Open Access
- 1 October 2020
- Published by Oxford University Press (OUP) in G3: Genes, Genomes, Genetics
- Vol. 10 (10) , 3565-3574
Construction of Pseudomolecules for the Chinese Chestnut (Castanea mollissima) Genome
The Chinese chestnut (Castanea mollissima Bl.) is a woody nut crop with a high ecological value. Although many cultivars have been selected from natural seedlings, elite lines with comprehensive agronomic traits and characters remain rare. To explore genetic resources with aid of whole genome sequence will play important roles in modern breeding programs for chestnut. In this study, we generated a high-quality C.mollissima genome assembly by combining 90× Pacific Biosciences long read and 170× high-throughput chromosome conformation capture data. The assembly was 688.93 Mb in total, with a contig N50 of 2.83 Mb. Most of the assembled sequences (99.75%) were anchored onto 12 chromosomes, and 97.07% of the assemblies were accurately anchored and oriented. A total of 33,638 protein-coding genes were predicted in the C.mollissima genome. Comparative genomic and transcriptomic analyses provided insights into the genes expressed in specific tissues, as well as those associated with burr development in the Chinese chestnut. This highly contiguous assembly of the C. mollissima genome provides a valuable resource for studies aiming at identifying and characterizing agronomical-important traits, and will aid the design of breeding strategies to develop more focused, faster, and predictable improvement programs.
- 1 October 2020
- Published by Oxford University Press (OUP) in G3: Genes, Genomes, Genetics
- Vol. 10 (10) , 3565-3574
Open AccessConstruction of Pseudomolecules for the Chinese Chestnut (Castanea mollissima) Genome
The Chinese chestnut (Castanea mollissima Bl.) is a woody nut crop with a high ecological value. Although many cultivars have been selected from natural seedlings, elite lines with comprehensive agronomic traits and characters remain rare. To explore genetic resources with aid of whole genome sequence will play important roles in modern breeding programs for chestnut. In this study, we generated a high-quality C.mollissima genome assembly by combining 90× Pacific Biosciences long read and 170× high-throughput chromosome conformation capture data. The assembly was 688.93 Mb in total, with a contig N50 of 2.83 Mb. Most of the assembled sequences (99.75%) were anchored onto 12 chromosomes, and 97.07% of the assemblies were accurately anchored and oriented. A total of 33,638 protein-coding genes were predicted in the C.mollissima genome. Comparative genomic and transcriptomic analyses provided insights into the genes expressed in specific tissues, as well as those associated with burr development in the Chinese chestnut. This highly contiguous assembly of the C. mollissima genome provides a valuable resource for studies aiming at identifying and characterizing agronomical-important traits, and will aid the design of breeding strategies to develop more focused, faster, and predictable improvement programs.