河南农业科学 ›› 2022, Vol. 51 ›› Issue (7): 1-12.DOI: 10.15933/j.cnki.1004-3268.2022.07.001
杨文庆1,刘天霞1,唐兴萍2,徐国富2,马喆2,杨贺凯3,吴文斗1
收稿日期:
2022-01-25
出版日期:
2022-07-15
发布日期:
2022-09-09
通讯作者:
吴文斗(1974-),男,云南凤庆人,副教授,硕士,主要从事农业大数据研究。E-mail:wuwd2004@126.com
作者简介:
杨文庆(1999-),女,云南临沧人,在读硕士研究生,研究方向:作物表型技术。E-mail:1976482590@qq.com
基金资助:
YANG Wenqing1,LIU Tianxia1,TANG Xingping2,XU Guofu2,MA Zhe2,YANG Hekai3,WU Wendou1
Received:
2022-01-25
Published:
2022-07-15
Online:
2022-09-09
摘要: 我国农业正处于由传统农业转向现代化农业的关键阶段,智慧农业是现代化农业发展的重要体现,是未来农业发展的必然趋势。智慧农业旨在将物联网、人工智能以及大数据等现代信息技术与传统农业深度结合,使农业生产实现智能化、绿色化、标准化、数字化。植物表型组学是研究植物表现型特征的科学,是智慧农业发展的关键技术之一,其通过采集细胞、器官、组织、植株以及群体各层面的表型数据,从海量数据中提取可重复性高、可信度高的重要性状信息,为基因挖掘、作物育种和农业生产过程精准管理提供数据支持和方法支撑。从表型数据采集、分析以及国内外植物表型分析平台建设方面综述了智慧农业背景下植物表型组学的发展现状,概述了植物表型组学研究在智慧农业生产中的应用,最后对植物表型组学未来发展趋势做出展望。
中图分类号:
杨文庆, 刘天霞, 唐兴萍, 徐国富, 马喆, 杨贺凯, 吴文斗. 智慧农业背景下的植物表型组学研究进展[J]. 河南农业科学, 2022, 51(7): 1-12.
YANG Wenqing, LIU Tianxia, TANG Xingping, XU Guofu, MA Zhe, YANG Hekai, WU Wendou. Research Progress on Plant Phenomics in the Context of Smart Agriculture[J]. Journal of Henan Agricultural Sciences, 2022, 51(7): 1-12.
[1]赵春江.植物表型组学大数据及其研究进展[J].农业大数据学报,2019,1(2):5‑18. ZHAO C J.Big data of plant phenomics and its research progress[J].Journal of Agricultural Big Data,2019,1(2):5‑18. [2]唐惠燕,倪峰,李小涛,等.基于Scopus的植物表型组学研究进展分析[J].南京农业大学学报,2018,41(6):1133‑1141. TANG H Y,NI F,LI X T,et al.Analysis of the advance in plant phenomics research based on Scopus tools[J].Journal of Nanjing Agricultural University,2018,41(6):1133‑1141. [3]晏小霞,邱燕连,王茂媛,等.海南产益智种质资源表型多样性及其与环境因子相关性研究[J].中药材,2020,43(11):2644‑2649. YAN X X,QIU Y L,WANG M Y,et al.Phenotypic diversity of alpinia oxyphylla germplasm resources in Hainan and its correlation with environmental factors[J].Journal of Chinese Medicinal Materials,2020,43(11):2644‑2649. [4]JOHANNSEN W. The genotype conception of heredity.1911[J].International Journal of Epidemiology,2014,43(4):989‑1000. [5]杨谨,杨涛.以表型组学促农业标准发展[C]//第十八届中国标准化论坛论文集.北京:《中国学术期刊(光盘版)》电子杂志社有限公司,2021:1569‑1573. YANG J,YANG T.Promoting the development of agricultural standards by phenomics[C]//Proceedings of the 18th China standardization forum. Beijing:“China Academic Journals(CD‑ROM Edition)” Electronic Magazine Co.,Ltd.,2021:1569‑1573. [6]玉光惠,方宣钧.表型组学的概念及植物表型组学的发展[J].分子植物育种,2009,7(4):639‑645. YU G H,FANG X J.Concept of phenomics and its development in plant science[J].Molecular Plant Breeding,2009,7(4):639‑645. [7]梁丽秀,杜传红,刘立才.植物表型组学在现代农业中的应用[J].科技创新与应用,2019(22):169‑171,174. LIANG L X,DU C H,LIU L C.Application of plant phenomics in modern agriculture[J].Technology Innovation and Application,2019(22):169‑171,174. [8]赵春江.智慧农业的发展现状与未来展望[J].华南农业大学学报,2021,42(6):1‑7. ZHAO C J.Current situations and prospects of smart agriculture[J].Journal of South China Agricultural University,2021,42(6):1‑7. [9]朱保芹,高艳丽,张晖,等.智慧农业背景下衡水市农产品高质量发展路径研究[J].南方农机,2021,52(22):94‑96. ZHU B Q,GAO Y L,ZHANG H,et al.Research on high quality development path of agricultural products in Hengshui under the background of smart agriculture[J].China Southern Agricultural Machinery,2021,52(22):94‑96. [10]杨有新,杨泽茂,吴才君,等.植物表型组学研究进展[J].江西农业大学学报,2015,37(6):1105‑1112. YANG Y X,YANG Z M,WU C J,et al.Advances in plant phenomics research[J].Acta Agriculturae Universitatis Jiangxiensis,2015,37(6):1105‑1112. [11]黄成龙,李曜辰,骆树康,等.基于结构光三维点云的棉花幼苗叶片性状解析方法[J].农业机械学报,2019,50(8):243‑248,288. HUAN C L,LI Y C,LUO S K,et al.Cotton seedling leaf traits extraction method from 3D point cloud based on structured light imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(8):243‑248,288. [12]胡伟娟,傅向东,陈凡,等.新一代植物表型组学的发展之路[J].植物学报,2019,54(5):558‑568. HU W J,FU X D,CHEN F,et al.A path to next generation of plant phenomics[J].Chinese Bulletin of Botany,2019,54(5):558‑568. [13]张慧春,周宏平,郑加强,等.植物表型平台与图像分析技术研究进展与展望[J].农业机械学报,2020,51(3):1‑17. ZHANG H C,ZHOU H P,ZHEN J Q,et al.Research progress and prospect in plant phenotyping platform and image analysis technology[J].Transactions of the Chinese Society for Agricultural Machinery,2020,51(3):1‑17. [14]GHOSAL S,BLYSTONE D,SINGH A K,et al.An explainable deep machine vision framework for plant stress phenotyping[J].Proceedings of the National Academy of Sciences of the United States of America,2018,115(18):4613‑4618. [15]FLORIAN G,ELIAS M,ANNEKATRIN M,et al.UAV‑based classification of cercospora leaf spot using RGB images[J].Drones,2021,5(2):34. [16]CADET É,SAMSON G.Detection and discrimination of nutrient deficiencies in sunflower by blue‑green and chlorophyll‑afluorescence imaging[J].Journal of Plant Nutrition,2011,34(14):2114‑2126. [17]SOUZA A,YANG Y.High‑throughput corn image segmentation and trait extraction using chlorophyll fluorescence images[J].Plant Phenomics,2021,3(1):15. [18]BAURIEGEL E,GIEBEL A,GEYER M,et al.Early detection of fusarium infection in wheat using hyper‑spectral imaging[J].Computers and Electronics in Agriculture,2011,75(2):304‑312. [19]田有文,程怡,王小奇,等.基于高光谱成像的苹果虫害检测特征向量的选取[J].农业工程学报,2014,30(12):132‑139. TIAN Y W,CHEN Y,WANG X Q,et al.Feature vectors determination for pest detection on apples based on hyperspectral imaging[J].Transaction of the CSAE,2014,30(12):132‑139. [20]徐小龙,蒋焕煜,杭月兰.热红外成像用于番茄花叶病早期检测的研究[J].农业工程学报,2012,28(5):145‑149. XU X L,JIANG H Y,HANG Y L.Study on detection of tomato mosaic disease at early stage based on infrared thermal imaging[J].Transaction of the CSAE,2012,28(5):145‑149. [21]CONRAD A O,LI W,LEE D Y,et al,Machine learning‑based presymptomatic detection of rice sheath blight using spectral profiles[J].Plant Phenomics,2020,2(1):314‑323. [22]MANICKAVASAGAN A,JAYAS D S,WHITE N D G.Thermal imaging to detect infestation by cryptolestes ferrugineus inside wheat kernels[J].Journal of Stored Products Research,2008,44(2):186‑192. [23]HE Y,XIAO S P,NIE P C,et al.Research on the optimum water content of detecting soil nitrogen using near infrared sensor[J].Sensors,2017,17(9):2045. [24]ANDO R,OZASA Y,GUO W.Robust surface reconstruction of plant leaves from 3D point clouds[J].Plant Phenomics,2021,3(1):28‑42. [25]LIU J G,XU X M,LIU Y H,et al.Quantitative potato tuber phenotyping by 3D imaging[J].Biosystems Engineering,2021,210:48‑59. [26]DEERY D M,REBETZKE G J,JIMENEZ‑BERNI J A,et al.Ground‑based LiDAR improves phenotypic repeatability of above‑ground biomass and crop growth rate in wheat[J].Plant Phenomics,2020,2(1):138‑148. [27]JIN S H,SU Y J,ZHANG Y G,et al.Exploring seasonal and circadian rhythms in structural traits of field maize from LiDAR time series[J].Plant Phenomics,2021,3(1):389‑403. [28]HUSIN N A,KHAIRUNNIZA‑BEJO S,ABDULLAH A F,et al.Application of ground‑based LiDAR for analysing oil palm canopy properties on the occurrence of basal stem rot(BSR)disease[J].Scientific Reports,2020,10(1):6464. [29]HU W J,ZHANG C,JIANG Y Q,et al.Nondestructive 3D image analysis pipeline to extract rice grain traits using X‑ray computed tomography[J].Plant Phenomics,2020,2(1):106‑117. [30]NATHAN H,KAREN A,CALLUM S P,et al.Non‑destructive,high‑content analysis of wheat grain traits using X‑ray micro computed tomography[J].Plant Methods,2017,13(1):1‑16. [31]MAIRHOFER S,PRIDMORE T,JOHNSON J,et al.X‑ray computed tomography of crop plant root systems grown in soil:X‑ray computed tomography of root systems[J].Current Protocols in Plant Biology,2017,2(4):270‑286. [32]MAJA M,LAURENT L,MIREILLE C,et al.A mobile NMR lab for leaf phenotyping in the field[J].Plant Methods,2017,13(1):53. [33]GUO Q H,WU F F,PANG S X,et a1.Crop 3D—a LiDAR based platform for 3D high‑throughput crop phenotyping[J].Science China(Life Sciences),2018,61(3):328‑339. [34]SHAFIEKHANI A,KADAM S,FRITSCHI F B,et al.Vinobot and Vinoculer:Two robotic platforms for high‑throughput field phenotyping[J].Sensors,2017,17(1):214. [35]周济,TARDIEU F,PRIDMORE T,等.植物表型组学:发展、现状与挑战[J].南京农业大学学报,2018,41 (4):580‑588. ZHOU J,TARDIEU F,PRIDMORE T,et al.Plant phenomics:History,present status and challenges[J].Journal of Nanjing Agricultural University,2018,41(4):580‑588. [36]BIERMAN A,LAPLUMM T,CADLE‑DAVIDSON L,et al.A high‑throughput phenotyping system using machine vision to quantify severity of grapevine powdery mildew[J].Plant Phenomics,2019,1(1):210‑222. [37]党满意,孟庆魁,谷芳,等.基于机器视觉的马铃薯晚疫病快速识别[J].农业工程学报,2020,36(2):193‑200. DANG M Y,MENG Q K,GU F,et al.Rapid recognition of potato late blight based on machine vision[J].Transaction of the CSAE,2020,36(2):193‑200. [38]刘媛,冯全.葡萄病害的计算机识别方法[J].中国农机化学报,2017,38(4):99‑104. LIU Y,FENG Q.Identification method of grape diseases based on computer technology[J].Journal of Chinese Agricultural Mechanization,2017,38(4):99‑104. [39]殷悦,张慧春,郑加强.基于双目立体视觉的植物三维重建系统[J].中国农机化学报,2021,42(3):129‑135. YIN Y,ZHANG H C,ZHENG J Q.Three dimensional reconstruction system of plant based on binocular stereo vision[J].Journal of Chinese Agricultural Mechanization,2021,42(3):129‑135. [40]杨乐,唐建军,何火娇.水稻根系空间分布特性的三维建模及可视化研究[J].江西农业大学学报,2016,38(3):588‑592. YANG L,TANG J J,HE H J.Three dimensional modeling and visualization of spatial distribution of rice root[J].Acta Agriculturae Universitatis Jiangxiensis,2016,38(3):588‑592. [41]孙茜,郑书河.基于双目视觉的植物三维重建方法及应用[J].安徽农业科学,2021,49(24):11‑17. SUN Q,ZHENG S H.Method of plant 3D reconstruction based on binocular vision and its application[J].Journal of Anhui Agricultural Sciences,2021,49(24):11‑17. [42]JUBERY T Z,CARLEY C N,SINGH A K,et al.Using machine learning to develop a fully automated soybean nodule acquisition pipeline(SNAP)[J].Plant Phenomics,2021,3(1):197‑208. [43]DIIXON L S,GODOY J V,CARTER A H.Evaluating the utility of carbon isotope discrimination for wheat breeding in the pacific northwest[J].Plant Phenomics,2019,1(1):69‑79. [44]GRINBLAT G L,UZAL L C,LARESE M G,et al.Deep learning for plant identification using vein morphological patterns[J].Computers and Electronics in Agriculture,2016,127:418‑424. [45]WEN G Q,MA B L,VANASSE A,et al.Machine learning‑based canola yield prediction for site‑specific nitrogen recommendations[J].Nutrient Cycling in Agroecosystems,2021,121(23):241‑256. [46]邓寒冰,许童羽,周云成,等.基于深度掩码的玉米植株图像分割模型[J].农业工程学报,2021,37(18):109‑120. DENG H B,XU T Y,ZHOU Y C,et al.Segmentation model for maize plant images based on depth mask[J].Transaction of the CSAE,2021,37(18):109‑120. [47]ELSAYED S,MISTELE B,SCHMIDHALTER U.Can changes in leaf water potential be assessed spectrally[J].Functional Plant Biology,2011,38(6):523‑533. [48]严佳豪,彭辰晨,陈超凡,等.基于机器视觉的植物表型研究现状[J].南方农机,2021,52(8):195‑196. YAN J H,PENG C C,CHEN C F,et al.Research status of plant phenotype based on machine vision[J].China Southern Agricultural Machinery,2021,52(8) :195‑196. [49]王鹤树,曹丽英.基于全卷积神经网络的植物叶片自动分割及表型解析[J].中国农机化学报,2021,42(8):161‑168. WANG H S,CAO L Y.Automatic segmentation and phenotypic analysis of plant leaves based on fully convolutional networks[J].Journal of Chinese Agricultural Mechanization,2021,42(8):161‑168. [50]史维杰,郝雅洁,赵明霞,等.基于三维重建技术的植物表型分析[J].物联网技术,2019,9(9):84‑86. SHI W J,HAO Y J,ZHAO M X,et al.Plant phenotype analysis based on three‑dimensional reconstruction technology[J].Internet of Things Technologies,2019,9(9):84‑86. [51]朱荣胜,李帅,孙永哲,等.作物三维重构技术研究现状及前景展望[J].智慧农业(中英文),2021,3(3):94‑115. ZHU R S,LI S,SUN Y Z,et al.Research advances and prospects of crop 3D reconstruction technology[J].Smart Agriculture,2021,3(3):94‑115. [52]REUZEAU C,FRANKARD V,HATZFELD Y,et al.TraitmillTM:A functional genomics platform for the phenotypic analysis of cereals[J].Plant Genetic Resources Characterization and Utilization,2006,4(1):20‑24. [53]段凌凤,杨万能.水稻表型组学研究概况和展望[J].生命科学,2016,28(10):1129‑1137. DUAN L F,YANG W N.Research advances and future scenarios of rice phenomics[J].Chinese Bulletin of Life Sciences,2016,28(10):1129‑1137. [54]胡伟娟,凌宏清,傅向东.植物表型组学研究平台建设及技术应用[J].遗传,2019,41(11):1060‑1066. HU W J,LING H Q,FU X D.Development and application of the plant phenomics analysis platform[J].Hereditas,2019,41(11):1060‑1066. [55]雷建椿,何金国.基于Ada Boost.M2和神经模糊系统的植物识别算法[J].计算机应用,2018,38(4):960‑964. LEI J C,HE J G.Plant recognition algorithm based on Ada Boost.M2 and neural fuzzy system[J].Journal of Computer Applications,2018,38(4):960‑964. [56]翁杨,曾睿,吴陈铭,等.基于深度学习的农业植物表型研究综述[J].中国科学(生命科学),2019,49(6):698‑716. WENG Y,ZENG R,WU C M,et al.A survey on deep‑learning‑based plant phenotype research in agriculture[J].Scientia Sinica(Vitae),2019,49(6):698‑716. [57]赵辉,曹宇航,岳有军,等.基于改进DenseNet的田间杂草识别[J].农业工程学报,2021,37(18):136‑142. ZAHO H,CAO Y H,YUE Y J,et al.Field weed recognition based on improved DenseNet[J].Transaction of the CSAE,2021,37(18):136‑142. [58]樊湘鹏,周建平,许燕,等.基于优化Faster R-CNN的棉花苗期杂草识别与定位[J].农业机械学报,2021,52(5):26‑34. FAN X P,ZHOU J P,XU Y,et al.Identification and localization of weeds based on optimized Faster R‑CNN in cotton seedling stage[J].Transactions of the Chinese Society for Agricultural Machinery,2021,52(5):26‑34. [59]任全会,杨保海.图像处理技术在田间杂草识别中应用研究[J].中国农机化学报,2020,41(6):154‑158. REN Q H,YANG B H.Application of image processing technology in weed recognition in field[J].Journal of Chinese Agricultural Mechanization,2020,41(6):154‑158. [60]苗中华,余孝有,徐美红,等.基于图像处理多算法融合的杂草检测方法及试验[J].智慧农业(中英文),2020,2(4):103‑115. MIAO Z H,YU X Y,XU M H,et al.Automatic weed detection method based on fusion of multiple image processing algorithms[J].Smart Agriculture,2020,2(4):103‑115. [61]岑海燕,朱月明,孙大伟,等.深度学习在植物表型研究中的应用现状与展望[J].农业工程学报,2020,36(9):1‑16. CEN H Y,ZHU Y M,SUN D W,et al.Current status and future perspective of the application of deep learning in plant phenotype research[J].Transaction of the CSAE,2020,36(9):1‑16. [62]PUIJALON S,BOUMA T J,DOUADY C J,et al.Plant resistance to mechanical stress:Evidence of an avoidance‑tolerance trade‑off[J].New Phytologist,2011,191(4):1141‑1149. [63]徐凌翔,陈佳玮,丁国辉,等.室内植物表型平台及性状鉴定研究进展和展望[J].智慧农业(中英文),2020,2(1):23‑42. XU L X,CHEN J W,DING G H,et al.Indoor phenotyping platforms and associated trait measurement:Progress and prospects[J].Smart Agriculture,2020,2(1):23‑42. [64]RAUSHER M D.Co‑evolution and plant resistance to natural enemies[J].Nature,2001,411:857‑864. [65]崔喜艳,张莹莹,周莹.植物响应干旱胁迫转录因子研究进展[J].吉林农业大学学报,2020,40(10):1792‑1806. CUI X Y,ZHANG Y Y,ZHOU Y.Research progress of plant transcription factors in response to drought stress [J].Journal of Jilin Agricultural University,2020,40(10):1792‑1806. [66]VIJAYARAHAVAREDDY P,VEMANAN R S,YIN X,et al.Acquired traits contribute more to drought tolerance in wheat than in rice[J].Plant Phenomics,2020,2(1):172‑187. [67]张慧春,杨琨琪,李杨先,等.面向植物抗旱性研究的多源表型信息采集和分析技术[J].农业机械学报,2022,53(2):203‑211. ZHANG H C,YANG K Q,LI Y X,et al.Multi‑source phenotypic information collection and analysis techniques for drought resistance of plants[J].Transactions of the Chinese Society for Agricultural Machinery,2022,53(2):203‑211. [68]赖金莉,李欣欣,薛磊,等.植物抗旱性研究进展[J].江苏农业科学,2018,46(17):23‑27. LAI J L,LI X X,XIE L,et al.Research progress on drought resistance of plants[J].Jiangsu Agricultural Sciences,2018,46(17):23‑27. [69]张凝,杨贵军,赵春江,等.作物病虫害高光谱遥感进展与展望[J].遥感学报,2021,25(1):403‑422. ZHANG N,YANG G J,ZHAO C J,et al.Progress and prospects of hyperspectral remote sensing technology for crop diseases and pests[J].Journal of Remote Sensing,2021,25(1):403‑422. [70]王一丁.烟草花叶病害高光谱特征及其病害程度判别分析模型的研究[D].郑州:河南农业大学,2016. WANG Y D.Research on hyperspectral characteristics of tobacco mosaic virus disease and its disease and discriminant analysis model of disease degree[D].Zhengzhou:Henan Agricultural University,2016. [71]NANSEN C,NGUYEN H D.Hyperspectral remote xsensing to detect leafminer‑induced stress in bok choy and spinach according to fertilizer regime and timing[J].Pest Management Science,2020,76(6):2208‑2216. [72]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. SUN R L,SUN Q,SUN C M,et al.Recent advances in remote sensing monitoring on wheat pests and diseases based on different platforms[J].Journal of Chinese Agricultural Mechanization,2021,42(3):142‑150. [73]孙盈蕊.基于多尺度遥感技术的水稻病虫害监测研究[D].北京:中国地质大学,2019. SUN Y R.Rice pest and disease monitoring based on multi‑scale remote sensing technology[D].Beijing:China University of Geosciences,2019. [74]陆百慧.基于气象和遥感数据的吉林省玉米病虫害研究[D].长春:吉林大学,2021. LU B H.Research on corn diseases and pests in Jilin province based on meteorological and remote sensing data[D].Changchun:Jilin University,2021. [75]LI D S,WANG R J,XIE C J,et al.A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network[J].Sensors,2020,20(3):578. [76]SLADOJEVIC S,ARSENOVIC M,ANDERLA A,et al.Deep neural networks based recognition of plant diseases by leaf image classification[J].Computational Intelligence and Neuroscience,2016,2016:1‑11. [77]PALOSUO T,KERSEBAUM K C,ANGULO C,et al.Simulation of winter wheat yield and its variability in different climates of Europe:A comparison of eight crop growth models[J].European Journal of Agronomy,2011,35(3):103‑114. [78]WU J,WU Q,PAGÈS L,et al.RhizoChamber‑Monitor:A robotic platform and software enabling characterization of root growth[J].Plant Methods,2018,14(1):1‑15. [79]ZHANG C,SI Y,LAMKEY J,et al.High‑throughput phenotyping of seed/seedling evaluation using digital image analysis[J].Agronomy,2018,8(5):1‑14. [80]李志军,杨圣慧,史德帅,等.基于轻量化改进YOLOv5 的苹果树产量测定方法[J].智慧农业(中英文),2021,3(2):100‑114. LI Z J,YANG S H,SHI D S,et al.Yield estimation method of apple tree based on improved lightweight YOLOv5[J].Smart Agriculture,2021,3(2):100‑114. [81]ALKHUDAYDI T,REYNOLDS D,GRIFFITHS S,et al.An exploration of deep‑learning based phenotypic analysis to detect spike regions in field conditions for UK bread wheat[J].Plant Phenomics,2019,1(1):162‑178. |
[1] | 于雁南, 莫泳彬, 严继池, 熊春林, 窦世卿, 杨荣峰. 基于改进ShuffleNet V2 的柑橘病害识别研究[J]. 河南农业科学, 2024, 53(1): 142-151. |
[2] | 钟正扬, 云利军, 杨璇玺, 陈载清. 基于改进YOLOX 的自然环境下核桃识别算法研究[J]. 河南农业科学, 2024, 53(1): 152-161. |
[3] | 李伟豪, 詹炜, 周婉, 韩涛, 王佩文, 刘虎, 熊梦园, 孙泳, . 轻量型Yolov7-TSA 网络在茶叶病害检测识别中的研究与应用[J]. 河南农业科学, 2023, 52(5): 162-169. |
[4] | 陈聪, 于啸, 宫琪. 基于改进残差网络的苹果叶片病害识别研究[J]. 河南农业科学, 2023, 52(4): 152-161. |
[5] | 臧贺藏, 王从胜, 赵巧丽, 赵晴, 张杰, 李国强, 郑国清. 基于深度学习的小麦倒伏自动分类方法研究[J]. 河南农业科学, 2023, 52(11): 167-173. |
[6] | 刁智华, 闫娇楠, 赵素娜, 贺振东. 基于图像处理的作物行识别算法研究进展[J]. 河南农业科学, 2022, 51(3): 12-19. |
[7] | 董燕, 李环宇, 李卫杰, 李春雷, 刘洲峰. 基于联合剪枝深度模型压缩的种子分选方法研究[J]. 河南农业科学, 2022, 51(1): 162-170. |
[8] | 郑二功,田迎芳,陈 涛. 基于深度学习的无人机影像玉米倒伏区域提取[J]. 河南农业科学, 2018, 47(8): 155-160. |
[9] | 徐 丽,杨 杰,王运祥,叶晋涛,马本学,吕 琛. 采后葡萄可溶性固形物含量的高光谱成像检测研究[J]. 河南农业科学, 2017, 46(3): 143-147. |
[10] | 陆宜清;杨松华;. 模糊综合评判在农业经营决策中的应用[J]. 河南农业科学, 2008, 37(12): 21-22. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||