基于无人机高通量植物表型大数据分析及应用研究综述
收稿日期: 2020-03-11
网络出版日期: 2020-10-30
基金资助
国家自然科学基金项目(61502236);中央高校基本科研业务费专项资金项目(KJQN201651);大学生创新创业训练专项计划项目(S20190025)
Analysis and Application of High-throughput Plant Phenotypic Big Data Collected from Unmanned Aerial Vehicles
Received date: 2020-03-11
Online published: 2020-10-30
植物表型是指基因和环境因素决定或影响的作物物理、生理、生化特征和性状。准确和快速的获取植物在各种不同环境条件下的表型信息，从而挖掘其基因组的遗传和表现规律，可有效推动有关基因组与表型信息关联性研究。无人机高通量植物表型平台凭借无人机机动灵活的特点，适合于农作物田间环境中的植物表型数据获取，具有数据获取效率高和成本低等优势，借助于图像、高光谱、激光雷达等先进传感器技术，为高效获取各类植物表型数据提供了可行的途径；与此同时，快速发展的大数据技术和智能数据分析技术为无人机所获取的植物表型图像提供有效的分析处理方法和技术。在此背景下，基于无人机平台的高通量植物表型分析，为研究田间作物表型信息提供了重要的方法和工具。本文综述了国内外无人机高通量作物表型大数据分析的最新研究成果，就其研究原理、相关算法、过程、关键技术及应用等进行总结与分析，重点讨论了应用于无人机高通量植物表型大数据分析相关的大数据处理与智能分析技术，重点分析了植物株高获取、叶面积指数、植物病害等典型的表型分析需求，并就其应用前景进行了总结和展望。
袁培森, 薛铭家, 熊迎军, 翟肇裕, 徐焕良 . 基于无人机高通量植物表型大数据分析及应用研究综述[J]. 农业大数据学报, 2021 , 3(3) : 62 -75 . DOI: 10.19788/j.issn.2096-6369.210307
Plant phenotypes refer to the physical, physiological and biochemical characteristics and traits that are determined or influenced by genes and environmental factors. Accurate and rapid access to plant phenotypic information under different environmental conditions, and the analysis of the genetic and performance patterns of their genomes, can effectively promote research on the correlation between genomic and phenotypic information. The Unmanned Aerial Vehicle (UAV) high-throughput plant phenotyping platform is suitable for acquiring plant phenotypic data in field environments owing to the UAV’s mobility and flexibility, and it has the great advantages of a high data acquisition efficiency and low cost. With the help of advanced sensor technologies, such as hyperspectral imaging and LIDAR, the UAV provides a feasible way to efficiently acquire plant phenotypic data. Effective analyses and processing methods and techniques for plant phenotypic data acquired by UAVs must be employed. Thus, high-throughput plant phenotypic analyses based on UAV platforms provides an important tool for studying plant phenotypic information from the field. This paper summarizes and analyzes the latest research results of UAV-based high-throughput crop phenotyping using big data analysis technology and artificial intelligence, as well as its research principles, relevant algorithms, processes, key technologies and applications. The main focus is on big data processing and intelligent analysis techniques related to UAV-based high-throughput plant phenotype big data and to the analysis of typical phenotypes, such as plant height, leaf area index, and plant diseases. We analyzed the current research needs and provide both a summary and outlook on related applications.
| 1 | Skaien C, Arcese P. Spatial variation in herbivory, climate and isolation predicts plant height and fruit phenotype in Plectritis congesta populations on islands[J]. Journal of Ecology, 2018, 106(6): 2344-2352. |
| 2 | 刘建刚，赵春江，杨贵军，等. 无人机遥感解析田间作物表型信息研究进展[J]. 农业工程学报, 2016, 32(24): 98-106. |
| 2 | Liu J G, Zhao C J, Yang G J, et al. Research Progress in Remote Sensing Analysis of Crop Phenotype information by UAV[J]. Agricultural Engineering, 2016, 32(24): 98-106. |
| 3 | Pajares G. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)[J]. Photogrammetric Engineering and Remote Sensing, 2015, 81(4): 281-329. |
| 4 | Groskinsky D K, Svensgaard J, Christensen S, et al. Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap[J]. Journal of Experimental Botany, 2015, 66(18): 5429-5440. |
| 5 | Coverdale T C, Mcgeary I J, Oconnell R D, et al. Strong but opposing effects of associational resistance and susceptibility on defense phenotype in an African savanna plant[J]. Oikos, 2019, 128(12): 1772-1782. |
| 6 | Paredes S H, Gao T, Law T F, et al. Design of synthetic bacterial communities for predictable plant phenotypes[J]. PLOS Biology, 2018, 16(2). |
| 7 | 潘锐，熊勤学，张文英. 数字图像技术及其在作物表型研究中的应用研究进展[J]. 长江大学学报（自科版）, 2016,(21): 38-41. |
| 7 | Pan R, Xiong Q X, Zhang W Y. Advances in Digital Image Technology and its Application in Crop Phenotype Research[J]. Journal of Changjiang University (self-taught edition), 2016,(21): 38-41. |
| 8 | 荆平平，李兵，贾宗仁，等. 基于无人机遥感的信息提取研究[J]. 测绘与空间地理信息, 2017, 40(12): 77-80. |
| 8 | Jing P P, Li B, Jia Z R, et al. Research on information Extraction based on UAV Remote Sensing[J]. Geomatics & Spatial information Technology, 2017, 40(12): 77-80. |
| 9 | Araus J L, Cairns J E. Field high-throughput phenotyping: the new crop breeding frontier[J]. Trends in Plant Science, 2014, 19(1): 52-61. |
| 10 | 李明，黄愉淇，李绪孟，等. 基于无人机遥感影像的水稻种植信息提取[J]. 农业工程学报, 2018, 34(04): 108-114. |
| 10 | Li M, Huang Y Q, Li X M, et al. Rice Planting Information Extraction based on UAV Remote Sensing Image[J]. Agricultural Engineering, 2018, 34(04): 108-114. |
| 11 | 张宏鸣，谭紫薇，韩文霆，等. 基于无人机遥感的玉米株高提取方法[J]. 农业机械学报, 2019, 50(05): 241-250. |
| 11 | Zhang H M, Tan Z W, Han W T, et al. Corn Plant Height Extraction Method based on UAV Remote Sensing[J]. Journal of agricultural machinery, 2019, 50(05): 241-250. |
| 12 | 胡鹏程. 基于无人机近感的高通量田间作物几何表型研究[D].北京：中国农业大学, 2018. |
| 12 | Hu P C, Study on Geometric Phenotypes of High-throughput Field Crops based on UAV Proximity[D], Beijing: China Agricultural University, 2018. |
| 13 | 牛庆林，冯海宽，杨贵军，等. 基于无人机数码影像的玉米育种材料株高和LAI监测[J]. 农业工程学报, 2018, 34(05): 73-82. |
| 13 | Niu Q L, Feng H K, Yang G J, et al. Plant Height and LAI Monitoring of Maize Breeding Materials based on UAV Digital Image[J]. Agricultural Engineering, 2018, 34(05): 73-82. |
| 14 | 刘帅兵，杨贵军，周成全，等. 基于无人机遥感影像的玉米苗期株数信息提取[J]. 农业工程学报, 2018, 34(22): 69-77. |
| 14 | Liu S B, Yang G J, Zhou C Q, et al. Data Extraction of Maize Seedling Number based on UAV Remote Sensing Image[J]. Data Extraction of Maize Seedling Number based on UAV Remote Sensing Image, 2018, 34(22): 69-77. |
| 15 | 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(08): 155-160. |
| 15 | Zheng E G, Tian Y F, Chen T. Deep Learning based UAV Image of Corn Lodging Area Extraction[J]. Journal of Henan Agricultural Sciences, 2018, 47(08): 155-160. |
| 16 | 张新乐，官海翔，刘焕军，等. 基于无人机多光谱影像的完熟期玉米倒伏面积提取[J]. 农业工程学报, 2019, 35(19): 98-106. |
| 16 | Zhang X L, Guan H X, Liu H J, et al. Extraction of Lodging Area of Mature Maize Based on Multi-Spectral Image of UAV[J]. Agricultural Engineering, 2019, 35(19): 98-106. |
| 17 | Tanger P, Klassen S P, Mojica J P, et al. Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice[J]. Scientific Reports, 2017, 7(1): 42839. |
| 18 | Tattaris M, Reynolds M P, Chapman S C. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding[J]. Frontiers in Plant Science, 2016, 7(1131): 1131. |
| 19 | Bai G, Ge Y, Hussain W, et al. A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding[J]. Computers and Electronics in Agriculture, 2016, 128: 181-192. |
| 20 | Hassan M A, Yang M, Rasheed A, et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform[J]. Plant Science, 2019, 282: 95-103. |
| 21 | 毛智慧，邓磊，赵晓明，等. 利用无人机遥感提取育种小区玉米倒伏信息[J]. 中国农学通报, 2019, 35(03): 62-68. |
| 21 | Mao Z H, Deng L, Zhao X M, et al. Using UAV Remote Sensing To Extract Lodging information of Maize in Breeding Plot[J]. Bulletin of Chinese Agronomy, 2019, 35(03): 62-68. |
| 22 | Juliana P, Montesinoslopez O A, Crossa J, et al. Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat[J]. Theoretical and Applied Genetics, 2019, 132(1): 177-194. |
| 23 | 赵立新，李繁茂，李彦，等. 基于无人机平台的直立作物倒伏监测研究展望[J]. 中国农机化学报, 2019, 40(11): 67-72. |
| 23 | Zhao L X, Li F M, Li Y, et al. Prospect of Vertical Crop Lodging Monitoring based on UAV Platform[J]. Chinese Journal of Agricultural Mechanization, 2019, 40(11): 67-72. |
| 24 | 范秀庆. 无人机免像控技术在地形图测量中的应用研究[J]. 测绘通报, 2017(S1): 66-68. |
| 24 | Fan X Q. Research on the Application of UAV Image-Free Control Technology in Topographic Map Measurement[J]. Chinese Journal of Cancer Research, 2017(S1): 66-68. |
| 25 | Scharr H, Pridmore T, Tsaftaris S A. Computer Vision Problems in Plant Phenotyping, CVPPP 2017: Introduction to the CVPPP 2017 Workshop Papers[C]// 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE, 2017. |
| 26 | Malambo L, Popescu S C, Murray S C, et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 64: 31-42. |
| 27 | Weiss M, Baret F. Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure[J]. Remote Sensing, 2017, 9(2): 111. |
| 28 | Patrick A, Li C. High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems[J]. Remote Sensing, 2017, 9(12): 1250. |
| 29 | Pircher M, Geipel J, Kusnierek K, et al. DEVELOPMENT OF A HYBRID UAV SENSOR PLATFORM SUITABLE FOR FARM-SCALE APPLICATIONS IN PRECISION AGRICULTURE[J]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017: 297-302. |
| 30 | Kashyap A, Ghose D. Pursuing a time varying and moving source signal using a sensor equipped UAV[C]// 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2017. |
| 31 | 汪沛，罗锡文，周志艳，等. 基于微小型无人机的遥感信息获取关键技术综述[J]. 农业工程学报, 2014, 30(18): 1-12. |
| 31 | Wang,Luo X W P, Zhou Z Y, et al. Research on Corn Planting information Extraction based on UAV Remote Sensing Technology[J]. Agricultural Engineering, 2014, 30(18): 1-12. |
| 32 | 李晓鹏，胡鹏程，徐照丽，等. 基于四旋翼无人机快速获取大田植株图像的方法及其应用[J]. 中国农业大学学报, 2017, 22(12): 131-137. |
| 32 | Li X P, Hu P C, Xu Z L, et al. Research on Corn Planting information Extraction based on UAV Remote Sensing Technology[J]. Journal of China Agricultural University, 2017, 22(12): 131-137. |
| 33 | 苏瑞东. 无人机在现代农业中的应用综述[J]. 江苏农业科学, 2019, 47(21): 75-79. |
| 33 | Su R D. the Application of UAV in Modern Agriculture[J]. Jiangsu Agricultural Science, 2019, 47(21): 75-79. |
| 34 | 王磊，周建平，许燕，等. 农用无人机的应用现状与展望[J]. 农药, 2019, 58(09): 625-630. |
| 34 | Wang L, Zhou J P, Xu Y, et al. Application Status and Prospect of Agricultural UAV[J]. Pesticide, 2019, 58(09): 625-630. |
| 35 | 曾易丽，彭森. 无人机技术在现代农业生产中的优势和缺陷分析[J]. 种子科技, 2019, 37(09): 140-142. |
| 35 | Ceng Y L, Peng S. Analysis of the Advantages and Disadvantages of UAV Technology in Modern Agricultural Production[J]. Seed Scicence and Technology, 2019, 37(09): 140-142. |
| 36 | Skobelev P, Budaev D, Gusev N,et al. Designing Multi-agent Swarm of UAV for Precise Agriculture[M]// Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. Springer, Cham, 2018. |
| 37 | ?edina Jaroslav, Pavelka K, Raeva P. UAV remote sensing capability for precision agriculture, forestry and small natural reservation monitoring[C]// SPIE Commercial + Scientific Sensing and Imaging. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2017. |
| 38 | Alsalam B H Y, Morton K, Campbell D,et al. Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture[C]// Aerospace Conference. IEEE, 2017. |
| 39 | Zhang Y, Zhang N. Imaging technologies for plant high-throughput phenotyping: a review[J]. Frontiers of Agricultural Science and Engineering, 2018, 5(4): 406-419. |
| 40 | 王洲，杨明欣，王新媛. 基于多传感器融合的多旋翼无人机近地面定位算法[J]. 成都信息工程大学学报, 2018, 33(03): 261-267. |
| 40 | Wang Z, Yang M X, Wang X Y. Near-Ground Positioning Algorithm of Multi-Rotor UAV based on Multi-Sensor Fusion[J]. Journal of Chengdu University of information Technology, 2018, 33(03): 261-267. |
| 41 | Paulus S. Measuring crops in 3D: using geometry for plant phenotyping[J]. Plant Methods, 2019, 15(1): 1-13. |
| 42 | Schonberger J L, Frahm J M. Structure-from-Motion Revisited[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016:4104-4113. |
| 43 | 张艳超，庄载椿，肖宇钊，等. 基于运动恢复结构算法的油菜NDVI三维分布[J]. 农业工程学报, 2015, 31(17): 207-214. |
| 43 | Zhang Y C, Zhuang Z C, Xiao Y Z. 3D Distribution of NDVI in Rapeseed based on Motion Recovery Structure Algorithm[J]. Agricultural Engineering, 2015, 31(17): 207-214. |
| 44 | 黄霞，郑顺义，桂力，等. 基于点云的谷粒高通量表型信息自动提取技术[J]. 农业机械学报, 2018, 49(04): 257-264. |
| 44 | Huang X, Zheng S Y, Gui L, et al. Automatic Extraction of High Throughput Phenotype information From Grain based on Point Cloud[J]. Journal of agricultural machinery, 2018, 49(04): 257-264. |
| 45 | Irschara A, Zach C, Frahm J M,et al. From structure-from-motion point clouds to fast location recognition[C]// 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA. IEEE, 2009. |
| 46 | 苗艳龙, 仇瑞承, 高阳,等. 基于车载三维激光雷达的玉米点云滤波研究[C]// 2018中国作物学会学术年会论文摘要集. 0. |
| 46 | Miao Y L, Chou R C, Gao Y, et al. Research on Corn Point Cloud Filter based on Vehicle-Mounted 3D Lidar[C].//.2018 Chinese Crop Society Academic Annual Conference Paper Abstract Collection. |
| 47 | 史维杰，张吴平，郝雅洁，等. 基于视觉三维重建的作物表型分析[J]. 湖北农业科学, 2019, 58(16): 125-128. |
| 47 | Shi W J, Zhang W P, Hao Y J, et al. Crop Phenotype Analysis based on Visual Three-Dimensional Reconstruction[J]. Hubei Agricultural Sciences, 2019, 58(16): 125-128. |
| 48 | Fawcett D, Azlan B, Hill T C, et al. Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation[J]. International Journal of Remote Sensing, 2019: 1-23. |
| 49 | Matese A, Gennaro S F D, Berton A. Assessment of a canopy height model CHM in a vineyard using UAV-based multispectral imaging[J]. International Journal of Remote Sensing, 2017, 38(8): 2150-2160. |
| 50 | 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(06): 698-716. |
| 50 | Weng Y, Ceng R, Wu C M, et al. a Review of Studies on Agricultural Plant Phenotypes based on Deep Learning[J]. Science in China: Life Science, 2019, 49(06): 698-716. |
| 51 | Cooper S D, Roy D P, Schaaf C B, et al. Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass[J]. Remote Sensing, 2017, 9(6): 531. |
| 52 | Q Q, N S, H B, et al. Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a "Phenomobile"[J]. Frontiers in plant science, 2019, 10: 554. |
| 53 | He J Q, Harrison R J, Li B. A novel 3D imaging system for strawberry phenotyping[J]. Plant Methods, 2017, 13(1): 93. |
| 54 | Chaudhury A, Barron J L. Machine Vision System for 3D Plant Phenotyping[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16(6). |
| 55 | 刘治开，牛亚晓，王毅，等. 基于无人机可见光遥感的冬小麦株高估算[J]. 麦类作物学报, 2019, 39(07): 859-866. |
| 55 | Liu Z K, Niu Y X, Wang Y, et al. Estimation of Plant Height of Winter Wheat based on Visible Remote Sensing of UAV[J]. Journal of Triticeae Crops, 2019, 39(07): 859-866. |
| 56 | Watanabe K, Guo W, Arai K, et al. High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling[J]. Frontiers in Plant Science, 2017, 8: 421. |
| 57 | Madec S, Baret F, De Solan B, et al. High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates[J]. Frontiers in Plant Science, 2017, 8: 2002. |
| 58 | Lottes P, Behley J, Chebrolu N, et al. Robust joint stem detection and crop breed classification using image sequences for plant specific treatment in precision farming[J]. Journal of Field Robotics, 2019. |
| 59 | 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, 2019: 9209727. |
| 60 | 杨国国，鲍一丹，刘子毅. 基于图像显著性分析与卷积神经网络的茶园害虫定位与识别[J]. 农业工程学报, 2017, 33(06): 156-162. |
| 60 | Yang G G, Bao Y D, Liu Z Y. Localization and Identification of Pests in Tea Garden based on Image Significance Analysis and Convolutional Neural Network[J]. Agricultural Engineering, 2017, 33(06): 156-162. |
| 61 | Ghosal S, Zheng B, Chapman S C, et al. A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting[J]. Plant Phenomics, 2019, 2019: 1525874. |
| 62 | Yang W, Yang C, Hao Z, et al. Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network[J]. IEEE Access, 2019, 7: 118239-118248. |
| 63 | Reiman D, Metwally A, Yang Dai. Using convolutional neural networks to explore the microbiome[J]. Conf Proc IEEE Eng Med Biol Soc, 2017, 2017:4269-4272. |
| 64 | Zhou Y, Xu T, Zheng W, et al. Classification and recognition approaches of tomato main organs based on DCNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(15): 219-226. |
| 65 | Fuentes A, Im D H, Yoon S, et al. Spectral Analysis of CNN for Tomato Disease Identification[C]. Berlin: Springer，2017. |
| 66 | Mardanisamani S, Maleki F, Kassani S H, et al. Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features.[J]. arXiv: Computer Vision and Pattern Recognition, 2019. |
| 67 | Chen F, Jahanshahi M R. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Na?ve Bayes Data Fusion[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4392-4400. |
| 68 | 苏伟，蒋坤萍，闫安，等. 基于无人机遥感影像的育种玉米垄数统计监测[J]. 农业工程学报, 2018, 34(10): 92-98. |
| 68 | Su W, Jiang K P, Yan A, et al. Statistical Monitoring of Ridge Number of Breeding Maize based on Remote Sensing Image of UAV[J]. Agricultural Engineering, 2018, 34(10): 92-98. |
| 69 | 和兴华. 基于卷积神经网络的玉米冠层图像分割与生育期鉴定方法[D]. 江西农业大学, 2018. |
| 69 | He X H, Image Segmentation and Growth Period Identification of Maize Canopy based on Convolutional Neural Network[D],Nanchang: Jiangxi Agricultural University，2018. |
| 70 | Toda Y, Okura F. How Convolutional Neural Networks Diagnose Plant Disease[J]. Plant Phenomics, 2019, 2019: 9237136. |
| 71 | 李旭冬，叶茂，李涛. 基于卷积神经网络的目标检测研究综述[J]. 计算机应用研究, 2017, 34(10): 2881-2886. |
| 71 | Li X D, Ye M, Li T. a Review of Target Detection based on Convolutional Neural Network[J]. Computer application research, 2017, 34(10): 2881-2886. |
| 72 | 许必宵，宫婧，孙知信. 基于卷积神经网络的目标检测模型综述[J]. 计算机技术与发展, 2019,(11): 1-8. |
| 72 | Xu B X, Gong J, Sun Z X. a Review of Target Detection Models based on Convolutional Neural Network[J]. Computer technology and development, 2019,(11): 1-8. |
| 73 | 常亮，邓小明，周明全，等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42(9): 1300-1312. |
| 73 | Chang L, Deng X, Zhou M, et al. Convolutional Neural Network in Image Comprehension[J]. Acta Automatica Sinica, 2016, 42(9): 1300-1312. |
| 74 | Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. 2012,25(2). |
| 75 | Oquab M, Bottou Léon, Laptev I,et al. Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks[C]// Computer Vision & Pattern Recognition. IEEE, 2014. |
| 76 | Hussain M, Bird J J, Faria D R. A Study on CNN Transfer Learning for Image Classification[C]// UK Workshop on Computational Intelligence. Springer, Cham, 2018. |
| 77 | Feng Y, Zeng S, Yang Y,et al. Study on the Optimization of CNN Based on Image Identification[C]// 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE Computer Society, 2018. |
| 78 | Liu K, Zhou Q, Wenbin W U, et al. Estimating the crop leaf area index using hyperspectral remote sensing[J]. Journal of Integrative Agriculture, 2016, 15(2): 475-491. |
| 79 | Forsmoo J, Anderson K, Macleod C J A, et al. Drone‐based structure‐from‐motion photogrammetry captures grassland sward height variability[J]. Journal of Applied Ecology, 2018, 55(6): 2587-2599. |
| 80 | 王凌，赵庚星，朱西存，等. 山丘区苹果树花期冠层反射率的定量遥感反演[J]. 应用生态学报, 2012, 23(08): 2233-2241. |
| 80 | Wang L, Zhao G X, Zhu X C, et al. Quantitative Remote Sensing inversion of Canopy Reflectance in Apple Tree Flowering Period in Mountain Area[J]. Journal of applying ecology, 2012, 23(08): 2233-2241. |
| 81 | 宗泽，张雪，郭彩玲，等. 基于骨架提取算法的作物表型参数提取方法[J]. 农业工程学报, 2015, 31(S2): 180-185. |
| 81 | Zong Z, Zhang X, Guo C L, et al. Crop Phenotypic Parameters Extraction Method based on Skeleton Extraction Algorithm[J]. Agricultural Engineering, 2015, 31(S2): 180-185. |
| 82 | 梁辉，刘汉湖，何敬. 基于无人机高光谱的水稻光合性能监测系统应用[J]. 农机化研究, 2020, 42(07): 214-218. |
| 82 | Liang H, Liu H H, He J, et al. Application of Rice Photosynthetic Performance Monitoring System based on UAVs[J]. Journal of Agricultural Mechanization Research, 2020, 42(07): 214-218. |
| 83 | 张经纬，贡亮，黄亦翔，等. 基于随机森林算法的黄瓜种子腔图像分割方法[J]. 农机化研究, 2017, 39(10): 163-168. |
| 83 | Zhang J W, Gong L, Huang Y X, et al. Image Segmentation Method of Cucumber Seed Cavity based on Random Forest Algorithm[J]. Journal of Agricultural Mechanization Research, 2017, 39(10): 163-168. |
| 84 | 刘斌，史云，吴文斌，等. 基于无人机遥感可见光影像的农作物分类[J]. 中国农业资源与区划, 2019, 40(08): 55-63. |
| 84 | Liu B, Shi Y, Wu W B, et al. Crop Classification based on Visible Image of UAV[J]. Agricultural resources and regionalization in China, 2019, 40(08): 55-63. |
| 85 | 李寒，张漫，高宇，等. 温室绿熟番茄机器视觉检测方法[J]. 农业工程学报, 2017, 33(S1): 328-334. |
| 85 | Li H, Zhang M, Gao Y, et al. Detecting Method of Greenhouse Green Ripe Tomato using Machine Vision[J]. Agricultural Engineering, 2017, 33(S1): 328-334. |
| 86 | 高林，杨贵军，李红军，等. 基于无人机数码影像的冬小麦叶面积指数探测研究[J]. 中国生态农业学报, 2016, 24(09): 1254-1264. |
| 86 | Gao L, Yang G J, Li H J, et al. Detection of Leaf Area Index of Winter Wheat based on Digital Image of UAV[J]. Chinese Journal of Eco-Agriculture, 2016, 24(09): 1254-1264. |
| 87 | 杨琦，叶豪，黄凯，等. 利用无人机影像构建作物表面模型估测甘蔗LAI[J]. 农业工程学报, 2017, 33(08): 104-111. |
| 87 | Yang Q, Ye H, Huang K. Constructed Crop Surface Models to estimate Sugarcane LAI using Drone Images[J]. Agricultural Engineering, 2017, 33(08): 104-111. |
| 88 | 韩文霆，李广，苑梦婵，等. 基于无人机遥感技术的玉米种植信息提取方法研究[J]. 农业机械学报, 2017, 48(01): 139-147. |
| 88 | Han W T, Li G, Yuan M C, et al. Research on Corn Planting information Extraction based on UAV Remote Sensing Technology[J]. Journal of agricultural machinery, 2017, 48(01): 139-147. |
| 89 | Coombes M, Eaton W, Chen W. Machine Vision for UAS Ground Operations[J]. Journal of Intelligent & Robotic Systems, 2017: 1-20. |
| 90 | 程雪，贺炳彦，黄耀欢，等. 基于无人机高光谱数据的玉米叶面积指数估算[J]. 遥感技术与应用, 2019, 34(04): 775-784. |
| 90 | Cheng X, He B Y, Huang Y H, et al. Estimation of Maize Leaf Area index based on Uas Hyperspectral Data[J]. Remote Sensing Technology and Application, 2019, 34(04): 775-784. |
| 91 | 白继伟，赵永超，张兵，等. 基于包络线消除的高光谱图像分类方法研究[J]. 计算机工程与应用, 2003(13): 88-90. |
| 91 | Bai J W, Zhao Y C, Zhang B, et al. Classification of Hyperspectral Images based on Envelope Elimination[J]. Computer Engineering and Application, 2003(13): 88-90. |
| 92 | 杜培军，夏俊士，薛朝辉，等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2): 236-256. DOI: 10.11834/jrs.20165022 |
| 92 | Du P J, Xia J S, Xue Z H, et al. Advances in Classification of Hyperspectral Remote Sensing Images[J]. Journal of Remote Sensing; Journal of Remote Sensing, 2016, 20(2): 236-256. |
| 93 | 周龙飞，张云鹤，成枢，等. 不同生育期倒伏胁迫下玉米叶面积指数高光谱响应解析[J]. 遥感技术与应用, 2019, 34(04): 766-774. |
| 93 | Zhou L F, Zhang Y H, Cheng S, et al. Response Analysis of Leaf Area index (LAI) Hyperspectral in Maize Under Lodging Stress At Different Growth Stages[J]. Remote Sensing Technology and Application, 2019, 34(04): 766-774. |
| 94 | 赵晓庆，杨贵军，刘建刚，等. 基于无人机载高光谱空间尺度优化的大豆育种产量估算[J]. 农业工程学报, 2017, 33(01): 110-116. |
| 94 | Zhao X Q, Yang G J, Liu J G, et al. Estimation of Soybean Breeding Yield based on UAV-borne Hyperspectral Spatial Scale Optimization[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(01): 110-116. |
| 95 | 孙刚，黄文江，陈鹏飞，等. 轻小型无人机多光谱遥感技术应用进展[J]. 农业机械学报, 2018, 49(03): 1-17. |
| 95 | Sun G, Huang W J, Chen P F, et al. Application Progress of Multi-spectral Remote Sensing Technology for Light and Small UAV[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(03): 1-17. |
| 96 | 兰玉彬，邓小玲，曾国亮. 无人机农业遥感在农作物病虫草害诊断应用研究进展[J]. 智慧农业, 2019, 1(02): 1-19. |
| 96 | Lan Y B, Deng X L, Zeng G L, et al. Advances in the Application of UAV Remote Sensing in the Diagnosis of Crop Diseases, Insect Pests and Weeds[J]. intelligent agriculture, 2019, 1(02): 1-19. |
/
| 〈 |
|
〉 |