基于文献计量学的农作物病虫害识别研究进展分析

doi:10.12190/j.issn.2096-1197.2023.04.15

Abstract

Abstract:

【Objective】To grasp the research progress on crop pests and diseases identification.【Methods】Using the literature on crop pests and diseases identification from Web of Science（WOS） and China National Knowledge Infrastructure（CNKI）databases as data sources，statistical analysis and bibliometric methods were used to analyze the research situation in the field of crop pests and diseases identification during 2000 to 2022 from the perspectives of literature publication volume，publication country，publication institution，research scholars，and research hotspots.【Results】2 214 English literature related to crop pests and diseases identification were retrieved. Since 2014，the publication volume increased continuously. The publication volume during 2014 to 2022 accounted for 85.0% of the total publication volume. 2 390 Chinese literature were retrieved，with a steady increase in publication volume from 2000 to 2012. After 2013，despite large fluctuations，there was an overall upward trend.China and the United States accounted for 41.3% of the total publication volume on crop pests and diseases identification research.In terms of collaboration and communication，China had close research collaboration with the United States and some Asian and European countries. The Chinese Academy of Sciences（90 publications） had the highest English literature publication volume，while the National Agricultural Technology Promotion Service Center（47 publications）had the highest Chinese literature publication volume.HUANG Wenjiang had significant research results both domestically and internationally（45 publications）. The main international research hotspots in the field of crop pests and diseases identification were the establishment of identification models and data acquisition.Domestic research hotspots underwent a transformation from manual recognition through expert systems to model optimization.【Conclusion】The current research momentum on crop pests and diseases identification domestically and internationally are still strong，with different research focuses.Intentionally，the research systems in model optimization and data acquisition methods are relatively mature. But there are still huge developmental potentials that can be excavated. Domestically，the research on intelligent recognition started relatively late but developed rapidly. In the future，the application of deep learning new algorithms may become a new research hotspot in the field of crop pets and diseases identification.

Key words: Crop, Pests and diseases identification, Bibliometrics, Research progress

CLC Number:

JIN Qingting, HUANG Hongxing, JIANG Shun. Analysis of the research progress on crop pests and diseases identification based on Bibliometrics[J].Journal of Northern Agriculture, 2023, 51(4): 122-134.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://journal30.magtechjournal.com/bfnyxb/EN/10.12190/j.issn.2096-1197.2023.04.15

https://journal30.magtechjournal.com/bfnyxb/EN/Y2023/V51/I4/122

Figures/Tables 13

Figure 1

Table 1

Figure 2

Table 2

Figure 3

Table 3

Figure 4

Figure 5

Figure 6

Table 4

Figure 7

Figure 8

Figure 9

References 43

[1]	SAVARY S, WILLOCQUET L, PETHYBRIDGE S J, et al. The global burden of pathogens and pests on major food crops[J]. Nature Ecology & Evolution, 2019, 3(3):430-439.
[2]	刘杰帆, 林铭佳, 张建桃, 等. 基于CiteSpace可视化分析的柑橘黄龙病研究进展[J]. 果树学报, 2023, 40(4):771-787.
[3]	曹丽英, 邸玉琦, 陈帅. 基于知识图谱的玉米病虫害研究可视化分析[J]. 东北农业科学, 2022, 47(1):145-150.
[4]	ZHU L J, MA Q, CHEN J, et al. Current progress on innovative pest detection techniques for stored cereal grains and thereof powders[J]. Food Chemistry, 2022, 396:133706. doi: 10.1016/j.foodchem.2022.133706
[5]	翟肇裕, 曹益飞, 徐焕良, 等. 农作物病虫害识别关键技术研究综述[J]. 农业机械学报, 2021, 52(7):1-18.
[6]	温艳兰, 陈友鹏, 王克强, 等. 基于机器视觉的病虫害检测综述[J]. 中国粮油学报, 2022, 37(10):271-279.
[7]	THELWALL M. Bibliometrics to webometrics[J]. Journal of Information Science, 2008, 34(4):605-621. doi: 10.1177/0165551507087238
[8]	ROCCHI L, BOGGIA A, PAOLOTTI L. Sustainable agricultural systems:A bibliometrics analysis of ecological modernization approach[J]. Sustainability, 2020, 12(22):9635. doi: 10.3390/su12229635
[9]	TAKEDA Y, KAJIKAWA Y. Tracking modularity in citation networks[J]. Scientometrics, 2010, 83(3):783-792. doi: 10.1007/s11192-010-0158-z
[10]	MEKHA J, PARTHASARATHY V. An automated pest identification and classification in crops using artificial intelligence a state of art review[J]. Automatic Control and Computer Sciences, 2022, 56(3):283-290. doi: 10.3103/S0146411622030038
[11]	HUANG M L, CHUANG T C, LIAO Y C. Application of transfer learning and image augmentation technology for tomato pest identification[J]. Sustainable Computing:Informatics and Systems, 2022, 33:100646. doi: 10.1016/j.suscom.2021.100646
[12]	MOHANTY S P, HUGHES D P, SALATHÉ M. Using deep learning for image-based plant disease detection[J]. Frontiers in Plant Science, 2016, 7:1419. pmid: 27713752
[13]	CHENG X, ZHANG Y H, CHEN Y Q, et al. Pest identification via deep residual learning in complex background[J]. Computers and Electronics in Agriculture, 2017, 141:351-356. doi: 10.1016/j.compag.2017.08.005
[14]	LIU J, WANG X W. Plant diseases and pests detection based on deep learning:A review[J]. Plant Methods, 2021, 17(1):1-18. doi: 10.1186/s13007-020-00700-7
[15]	WANG K L, CHEN K Y, DU H Y, et al. New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models[J]. Ecological Informatics, 2022, 69:101620. doi: 10.1016/j.ecoinf.2022.101620
[16]	XIAO Z T, YIN K, GENG L, et al. Pest identification via hyperspectral image and deep learning[J]. Signal,Image and Video Processing, 2022, 16(4):873-880. doi: 10.1007/s11760-021-02029-7
[17]	WANG B. Identification of crop diseases and insect pests based on deep learning[J]. Scientific Programming, 2022, 2022:1-10.
[18]	CARDIM FERREIRA LIMA M, DAMASCENA DE ALMEIDA LEANDRO M E, VALERO C, et al. Automatic detection and monitoring of insect pests:A review[J]. Agriculture, 2020, 10(5):161. doi: 10.3390/agriculture10050161
[19]	ABD EL-GHANY N M, ABD EL-AZIZ S E, MAREI S S. A review:Application of remote sensing as a promising strategy for insect pests and diseases management[J]. Environmental Science and Pollution Research, 2020, 27(27):33503-33515. doi: 10.1007/s11356-020-09517-2
[20]	CALOU V B C, DOS SANTOS TEIXEIRA A, MOREIRA L C J, et al. The use of UAVs in monitoring yellow sigatoka in banana[J]. Biosystems Engineering, 2020, 193:115-125. doi: 10.1016/j.biosystemseng.2020.02.016
[21]	DU X, LI Q Z, SHANG J L, et al. Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain[J]. GIScience & Remote Sensing, 2019, 56(7):1093-1113.
[22]	YANG N, YU J J, WANG A Y, et al. A rapid rice blast detection and identification method based on crop disease spores′ diffraction fingerprint texture[J]. Journal of the Science of Food and Agriculture, 2020, 100(9):3608-3621. doi: 10.1002/jsfa.v100.9
[23]	章艺, 马新焱, 余红瑞, 等. 十字花科作物根肿病综合防治研究进展[J]. 中国蔬菜, 2022(10):27-37.
[24]	牛学德, 高丙朋, 任荣荣, 等. 基于轻量级CNN的作物病虫害识别及安卓端应用[J]. 广西师范大学学报(自然科学版), 2022, 40(6):59-68.
[25]	周维, 牛永真, 王亚炜, 等. 基于改进的YOLOv4-GhostNet水稻病虫害识别方法[J]. 江苏农业学报, 2022, 38(3):685-695.
[26]	赵海岚, 蒙继华, 纪云鹏. 遥感技术在苹果园精准种植管理中的应用现状及展望[J]. 自然资源遥感, 2022, 35(2):1-15.
[27]	邓小玲, 曾国亮, 朱梓豪, 等. 基于无人机高光谱遥感的柑橘患病植株分类与特征波段提取[J]. 华南农业大学学报, 2020, 41(6):100-108.
[28]	孙瑞琳, 孙全, 孙成明, 等. 基于不同平台的小麦病虫害遥感监测研究进展[J]. 中国农机化学报, 2021, 42(3):142-150. doi: 10.13733/j.jcam.issn.2095-5553.2021.03.020
[29]	USHA K, SINGH B. Potential applications of remote sensing in horticulture:A review[J]. Scientia Horticulturae, 2013, 153:71-83. doi: 10.1016/j.scienta.2013.01.008
[30]	AHMAD LOTI N N, MOHD NOOR M R, CHANG S W. Integrated analysis of machine learning and deep learning in chili pest and disease identification[J]. Journal of the Science of Food and Agriculture, 2021, 101(9):3582-3594. doi: 10.1002/jsfa.10987 pmid: 33275806
[31]	ZHAO J, CHENG X P. Field pest identification by an improved Gabor texture segmentation scheme[J]. New Zealand Journal of Agricultural Research, 2007, 50(5):719-723. doi: 10.1080/00288230709510343
[32]	THENMOZHI K, SRINIVASULU REDDY U. Crop pest classification based on deep convolutional neural network and transfer learning[J]. Computers and Electronics in Agriculture, 2019, 164:104906. doi: 10.1016/j.compag.2019.104906
[33]	杨田堂, 刘琳. 保护地辣椒主要病害症状识别及综合防治[J]. 北方园艺, 2003(2):64-65.
[34]	刘琳. 大棚春番茄主要病害的识别与防治[J]. 北方园艺, 2003(6):16-17.
[35]	高灵旺, 陈继光, 于新文, 等. 农业病虫害预测预报专家系统平台的开发[J]. 农业工程学报, 2006, 22(10):154-158.
[36]	王婧, 路宗海, 李会平, 等. 基于Web的园林植物病虫害诊断系统框架构建[J]. 北方园艺, 2016(7):205-208.
[37]	邓晓璐, 王培, 马宁, 等. 基于物联网的寒地玉米大斑病预警系统的设计与实现[J]. 中国农机化学报, 2016, 37(7):166-170.
[38]	秦彩杰, 李勇. 作物病虫害自动识别技术研究:基于视频监控和无人机平台[J]. 农机化研究, 2021, 43(10):42-45.
[39]	伏荣桃, 陈诚, 王剑, 等. 植保无人机对水稻病虫害防治条件与防效的研究[J]. 中国农业科技导报, 2021, 23(4):103-109. doi: 10.13304/j.nykjdb.2019.0682
[40]	王兴旺, 郑汉垣, 王素青. 基于CSJMM-AS-GAC的马陆葡萄病虫害识别研究[J]. 河南农业科学, 2022, 51(6):154-163.
[41]	彭红星, 何慧君, 高宗梅, 等. 基于改进ShuffleNetV2模型的荔枝病虫害识别方法[J]. 农业机械学报, 2022, 53(12):290-300.
[42]	彭红星, 徐慧明, 刘华鼐. 融合双分支特征和注意力机制的葡萄病虫害识别模型[J]. 农业工程学报, 2022, 38(10):156-165.
[43]	刘斌, 贾润昌, 朱先语, 等. 面向移动端的苹果叶部病虫害轻量级识别模型[J]. 农业工程学报, 2022, 38(6):130-139.

Analysis of the research progress on crop pests and diseases identification based on Bibliometrics

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 43

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LIU Xue, LIANG Suyu, WANG Qiyao. Domestic and international research progress in saline soil improvement based on bibliometrics [J]. Journal of Northern Agriculture, 2023, 51(5): 123-134.
[2]	BAO Junwei, Wulantuya , CHE Youwei, LIU Zhaohui, LIU Zhaoxia. Research on crop planting structure extraction methods based on GF-6 images [J]. Journal of Northern Agriculture, 2023, 51(4): 112-121.
[3]	WANG Yuwen, YUAN Jingcheng, ZHONG Xin, YAO Xuhang, FAN Guangyu, LIU Yinghui, ZHAO Zhihai. Research progress in nutritional function of millet [J]. Journal of Northern Agriculture, 2022, 50(6): 113-118.
[4]	JIANG Wei, XUE Guoping, BAI Hongmei, DU Jinwei, ZHU Chunxia, LI Jie, SONG Qingcheng, JI Shuli, WANG Lisheng. Effects of different treatments on the bacterial community composition and diversity in cucumber continuous cropping soil [J]. Journal of Northern Agriculture, 2022, 50(2): 28-37.
[5]	SU Yajie, TIAN Xiaoyan, DU Lei, BAI Quanjiang, LIU Zhida, ZHAO Jun, XU Jiali, DUAN Xiaojun, WU Zhanmin, YUN Xiaopeng. Screening trap crops of sunflower broomrape（Orobanche cumana Wallr.）in Bayannur [J]. Journal of Northern Agriculture, 2022, 50(2): 103-109.
[6]	WANG Xiawen, WANG Feifei, JIN Qian, CHEN Weijun, CHEN Chun, YU Chenggong. Selection of suitable japonica rice varieties（lines） for rice and shrimp co-cropping model in Northern Jiangsu [J]. Journal of Northern Agriculture, 2021, 49(6): 58-64.
[7]	GAO Hongyan, ZHAO Peiyi, DUAN Yu, REN Yongfeng, ZHANG Peng, GAO Yu, GAO Riping, HAN Yunfei, LIU Xiaoyue. General planting situation and development potential of green manure crops in Inner Mongolia [J]. Journal of Northern Agriculture, 2021, 49(6): 65-70.
[8]	WU Xuemei, XU Hongying, GAO Jingxia. Effects of combined application of organic fertilizer and corn straw on growth,yield and quality of continuous cropping pepper in tunnel [J]. Journal of Northern Agriculture, 2021, 49(6): 114-120.
[9]	YUAN Yuwei, ZHANG Lei, FU Chongyi, SUN Pingping, WU Zhanmin, MA Qiang, LI Zhengnan. Research progress of three latent viruses in apple [J]. Journal of Northern Agriculture, 2021, 49(4): 83-97.
[10]	BAO Junwei, YU Lifeng, Wulantuya, XU Hongtao, YU Weizhuo, DUN Huixia. Application of crop type identification method based on ESTARFM model [J]. Journal of Northern Agriculture, 2021, 49(3): 128-134.
[11]	MIAO Sanming, DANG Jiuzhan, ZHAO Xiaoyu, FAN Xiurong, SUN Xiangchun, WANG Xuejiao, LI Qiang, ZHANG Yan, ZHANG Yujin. Study on the optimum nitrogen application rate of soybean intercropping with double-plant maize in one hole in Hetao irrigation area [J]. Journal of Northern Agriculture, 2021, 49(2): 31-35.
[12]	Wulantuya, YU Lifeng, BAO Junwei, XU Hongtao, Wuyundeji, REN Tingting, ZHAO Jiale, DUN Huixia, YU Weizhuo. Large scale extraction of main crops growing area based on remote sensing geographic feature curve [J]. Journal of Northern Agriculture, 2021, 49(1): 127-134.
[13]	YU Lifeng, Wulantuya, LI Jihui, YU Weizhuo, DUN Huixia. Multi-source remote sensing data feature optimization for different crop extraction in Daxing′Anling along the foothills [J]. Journal of Northern Agriculture, 2020, 48(6): 119-128.
[14]	ZHANG Haibin, MENG Meilian, CHEN Youjun, YE Jun, SHAO Ying, ZHANG Tingting, MENG Lili. Effects of different preceding crops on potato dry matter accumulation and yield in the northern of Yinshan Mountain [J]. Journal of Northern Agriculture, 2020, 48(5): 10-16.
[15]	KONG Qingquan, ZHAO Cunhu, HE Xiaoyong, CHEN Wenjin, TIAN Xiaoyan, ZHANG Ziqiang, HONG Zhong, YANG Jie, CHEN Jinglian, GUO Xiaoqing. Agronomic traits and benefit analysis of fresh food bean after wheat replanting [J]. Journal of Northern Agriculture, 2020, 48(5): 17-20.