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| Application of low-altitude unmanned aerial vehicle remote sensing in the phenotypic analysis of oil crops |
Yongqi SUN1( ),Mengyuan CHEN2,Qian HUANG1,Kangni ZHANG1,Bing WANG3,Fei LIU2,4,Weijun ZHOU1,4() |
1.College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, Zhejiang, China
2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China
3.Zhejiang Big Data Development Center for Agriculture and Rural Affairs, Hangzhou 310020, Zhejiang, China
4.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, Zhejiang, China |
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Abstract The conventional approach to gathering field phenotypic data for oil crops is characterized by its time-consuming and labor-intensive nature, resulting in low work efficiency. Conversely, low-altitude unmanned aerial vehicle (UAV) remote sensing offers numerous advantages, including rapidity, convenience, low cost, and ease of manipulation. This technology enhances the precision of morphological parameters and physiological and biochemical indicators of oil crops measured by remote sensing in small- and medium-scale areas, thereby enabling the initial attainment of field growth information for oil crops. Furthermore, it facilitates the swift acquisition, processing, and analysis of such data. This study provided a comprehensive overview of the advancements made in domestic and foreign low-altitude UAV remote sensing for oil crops, including rape, soybean, peanut, sunflower, and oil palm, and it introduced the prevailing UAV flight platforms, airborne sensors, and operating procedures, and it focused on combing the application of UAV remote sensing in morphological analysis, detection of physiological and biochemical indicators, yield estimation, and monitoring of adversity stress in recent years. Furthermore, it identified the limitations and future prospects of low-altitude UAV remote sensing in the domain of oil crop monitoring, aiming to provide a theoretical basis for the subsequent development and accurate implementation of smart agriculture.
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Received: 20 April 2023
Published: 29 August 2023
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Corresponding Authors:
Weijun ZHOU
E-mail: yq_sun@zju.edu.cn;wjzhou@zju.edu.cn
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低空无人机遥感在油料作物表型分析中的应用
传统的油料作物田间表型数据采集方法费时费力，工作效率低。低空无人机遥感具有快速便捷、成本低、易操控等优势，提高了在中、小尺度区域遥感观测油料作物的形态学参数和生理生化指标的精细化程度，初步实现了油料作物田间生长信息的快速采集、处理与分析应用。本文综述了近年来国内外低空无人机遥感在油菜、大豆、花生、向日葵、油棕等油料作物表型分析上的研究进展，介绍了当前主流的无人机飞行平台、机载传感器以及作业流程，重点梳理了无人机遥感在油料作物形态学分析、生理生化指标检测、产量估测以及逆境胁迫监测等多方面的应用情况，指出了低空无人机遥感在油料作物监测领域存在的不足和未来的发展趋势，以期为智慧农业的后续发展和精准应用提供理论依据。
关键词：
无人机,
低空遥感,
油料作物,
表型分析,
长势监测
|
|
| [1] |
FENG L, CHEN S S, ZHANG C, et al. A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping[J]. Computers and Electronics in Agriculture, 2021, 182: 106033. DOI: 10.1016/j.compag.2021.106033
doi: 10.1016/j.compag.2021.106033
|
|
|
| [2] |
SINGH A, JONES S, GANAPATHYSUBRAMANIAN B, et al. Challenges and opportunities in machine-augmented plant stress phenotyping[J]. Trends in Plant Science, 2021, 26(1): 53-69. DOI: 10.1016/j.tplants.2020.07.010
doi: 10.1016/j.tplants.2020.07.010
|
|
|
| [3] |
陈鹏飞.无人机在农业中的应用现状与展望[J].浙江大学学报(农业与生命科学版),2018,44(4):399-406. DOI:10.3785/j.issn.1008-9209.2017.12.150
CHEN P F. Applications and trends of unmanned aerial vehicle in agriculture[J]. Journal of Zhejiang University (Agriculture & Life Sciences), 2018, 44(4): 399-406. (in Chinese with English abstract)
doi: 10.3785/j.issn.1008-9209.2017.12.150
|
|
|
| [4] |
朱红艳.基于无人机低空遥感的油菜表型信息获取方法研究[D].浙江,杭州:浙江大学,2019.
ZHU H Y. Acquisition and methodology of oilseed rape phenotyping based on low-altitude remote sensing of UAV[D]. Hangzhou, Zhejiang: Zhejiang University, 2019. (in Chinese with English abstract)
|
|
|
| [5] |
康恺,张伟,贺燕,等.大豆冠层叶片氮含量检测研究:基于无人机多光谱图像[J].农机化研究,2024,46(2):151-156. DOI:10.13427/j.cnki.njyi.2024.02.009
KANG K, ZHANG W, HE Y, et al. Study on nitrogen content detection of soybean canopy: based on multispectral image of UAV[J]. Journal of Agricultural Mechanization Research, 2024, 46(2): 151-156. (in Chinese with English abstract)
doi: 10.13427/j.cnki.njyi.2024.02.009
|
|
|
| [6] |
WANG N, SIEGMANN B, RASCHER U, et al. Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over struc-turally different crops[J]. Agricultural and Forest Meteorology, 2022, 323: 109081. DOI: 10.1016/j.agrformet.2022.109081
doi: 10.1016/j.agrformet.2022.109081
|
|
|
| [7] |
WANG N, CLEVERS J G P W, WIENEKE S, et al. Potential of UAV-based sun-induced chlorophyll fluorescence to detect water stress in sugar beet[J]. Agricultural and Forest Meteo-rology, 2022, 323: 109033. DOI: 10.1016/j.agrformet.2022.109033
doi: 10.1016/j.agrformet.2022.109033
|
|
|
| [8] |
杨国峰,何勇,冯旭萍,等.无人机遥感监测作物病虫害胁迫方法与最新研究进展[J].智慧农业(中英文),2022,4(1):1-16. DOI:10.12133/j.smartag.SA202201008
YANG G F, HE Y, FENG X P, et al. Methods and new research progress of remote sensing monitoring of crop disease and pest stress using unmanned aerial vehicle[J]. Smart Agriculture, 2022, 4(1): 1-16. (in Chinese with English abstract)
doi: 10.12133/j.smartag.SA202201008
|
|
|
| [9] |
王庆,车荧璞,柴宏红,等.基于无人机可见光与激光雷达的甜菜株高定量评估[J].农业机械学报,2021,52(3):178-184. DOI:10.6041/j.issn.1000-1298.2021.03.019
WANG Q, CHE Y P, CHAI H H, et al. Quantitative evaluation of sugar beet plant height based on UAV-RGB and UAV-LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(3): 178-184. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2021.03.019
|
|
|
| [10] |
WAN L, ZHOU W J, HE Y, et al. Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets[J]. Remote Sensing of Environment, 2022, 269: 112826. DOI: 10.1016/j.rse.2021.112826
doi: 10.1016/j.rse.2021.112826
|
|
|
| [11] |
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. DOI:10.11975/j.issn.1002-6819.2020.09.001
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]. Transactions of the CSAE, 2020, 36(9): 1-16. (in Chinese with English abstract)
doi: 10.11975/j.issn.1002-6819.2020.09.001
|
|
|
| [12] |
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. DOI: 10.1016/j.compag.2016.07.003
doi: 10.1016/j.compag.2016.07.003
|
|
|
| [13] |
杨玉莹.基于多源遥感数据的大豆种植区提取方法研究[D].安徽,合肥:安徽大学,2021.
YANG Y Y. Research on extraction method of soybean planting area based on multi-source remote sensing data[D]. Hefei, Anhui: Anhui University, 2021. (in Chinese with English abstract)
|
|
|
| [14] |
ZHAO B Q, ZHANG J, YANG C H, et al. Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery[J]. Frontiers in Plant Science, 2018, 9: 1362. DOI: 10.3389/fpls.2018.01362
doi: 10.3389/fpls.2018.01362
|
|
|
| [15] |
ZHANG J, ZHAO B Q, YANG C H, et al. Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks[J]. Frontiers in Plant Science, 2020, 11: 617. DOI: 10.3389/fpls.2020.00617
doi: 10.3389/fpls.2020.00617
|
|
|
| [16] |
宋志双.基于无人机多光谱遥感影像的向日葵农田信息获取[D].陕西,杨凌:西北农林科技大学, 2021.
SONG Z S. Information acquisition of sunflower farmland based on UAV multispectral remote sensing images[D]. Yangling, Shaanxi: Northwest A&F University, 2021. (in Chinese with English abstract)
|
|
|
| [17] |
李婕,李毅,张瑞杰,等.无人机遥感影像在油菜品种识别中的应用[J].江苏农业学报,2022,38(3):675-684. DOI:10.3969 /j.issn.1000-4440.2022.03.013
LI J, LI Y, ZHANG R J, et al. Application of UAV remote sensing image in rape variety identification[J] Jiangsu Journal of Agricultural Sciences, 2022, 38(3): 675-684. (in Chinese with English abstract)
doi: 10.3969 /j.issn.1000-4440.2022.03.013
|
|
|
| [18] |
LIN Y D, CHEN T T, LIU S Y, et al. Quick and accurate monitoring peanut seedlings emergence rate through UAV video and deep learning[J]. Computers and Electronics in Agriculture, 2022, 197: 106938. DOI: 10.1016/j.compag.2022.106938
doi: 10.1016/j.compag.2022.106938
|
|
|
| [19] |
SUN Z, GUO X Y, XU Y, et al. Image recognition of male oilseed rape (Brassica napus) plants based on convolutional neural network for UAAS navigation applications on supple-mentary pollination and aerial spraying[J]. Agriculture, 2022, 12(1): 62. DOI: 10.3390/agriculture12010062
doi: 10.3390/agriculture12010062
|
|
|
| [20] |
WAN L, ZHU J P, DU X Y, et al. A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles[J]. Journal of Experimental Botany, 2021, 72(13): 4691-4707. DOI: 10.1093/jxb/erab194
doi: 10.1093/jxb/erab194
|
|
|
| [21] |
YANG Q, SHE B, HUANG L S, et al. Extraction of soybean planting area based on feature fusion technology of multi-source low altitude unmanned aerial vehicle images[J]. Ecological Informatics, 2022, 70: 101715. DOI: 10.1016/j.ecoinf.2022.101715
doi: 10.1016/j.ecoinf.2022.101715
|
|
|
| [22] |
ZHONG Y F, HU X, LUO C, et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 2020, 250: 112012. DOI: 10.1016/j.rse.2020.112012
doi: 10.1016/j.rse.2020.112012
|
|
|
| [23] |
HUANG S J, HAN W T, CHEN H P, et al. Recognizing zucchinis intercropped with sunflowers in UAV visible images using an improved method based on OCRNet[J]. Remote Sensing, 2021, 13(14): 2706. DOI: 10.3390/rs13142706
doi: 10.3390/rs13142706
|
|
|
| [24] |
BAI Y, NIE C W, WANG H W, et al. A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery[J]. Precision Agriculture, 2022, 23(5): 1720-1742. DOI: 10.1007/s11119-022-09907-1
doi: 10.1007/s11119-022-09907-1
|
|
|
| [25] |
BONET I, CARAFFINI F, PEÑA A, et al. Oil palm detection via deep transfer learning[C]//Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC 2020). Glasgow, UK: IEEE, 2020.
|
|
|
| [26] |
张建,谢田晋,尉晓楠,等.无人机多角度成像方式的饲料油菜生物量估算研究[J].作物学报,2021,47(9):1816-1823. DOI:10.3724/SP.J.1006.2021.04211
ZHANG J, XIE T J, WEI X N, et al. Estimation of feed rapeseed biomass based on multi-angle oblique imaging technique of unmanned aerial vehicle[J]. Acta Agronomica Sinica, 2021, 47(9): 1816-1823. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.04211
|
|
|
| [27] |
SARKAR S, CAZENAVE A B, OAKES J, et al. High-throughput measurement of peanut canopy height using digital surface models[J]. The Plant Phenome Journal, 2020, 3: e20003. DOI: 10.1002/ppj2.20003
doi: 10.1002/ppj2.20003
|
|
|
| [28] |
TEODORO P E, TEODORO L P R, BAIO F H R, et al. Predicting days to maturity, plant height, and grain yield in soybean: a machine and deep learning approach using multi-spectral data[J]. Remote Sensing, 2021, 13(22): 4632. DOI: 10.3390/rs13224632
doi: 10.3390/rs13224632
|
|
|
| [29] |
AVTAR R, SUAB S A, SYUKUR M S, et al. Assessing the influence of UAV altitude on extracted biophysical parameters of young oil palm[J]. Remote Sensing, 2020, 12(18): 3030. DOI: 10.3390/rs12183030
doi: 10.3390/rs12183030
|
|
|
| [30] |
SONG Z S, ZHANG Z T, YANG S Q, et al. Identifying sunflower lodging based on image fusion and deep semantic segmentation with UAV remote sensing imaging[J]. Computers and Electronics in Agriculture, 2020, 179: 105812. DOI: 10.1016/j.compag.2020.105812
doi: 10.1016/j.compag.2020.105812
|
|
|
| [31] |
章凌翔.基于无人机多光谱影像油菜产量预测及倒伏风险评价[D].陕西,杨凌:西北农林科技大学,2022.
ZHANG L X. Rape yield prediction and lodging risk assess-ment based on UAV multispectral image[D]. Yangling, Shaanxi: Northwest A&F University, 2022. (in Chinese with English abstract)
|
|
|
| [32] |
ZHANG Y, YANG Y Z, ZHANG Q W, et al. Toward multi-stage phenotyping of soybean with multimodal UAV sensor data: a comparison of machine learning approaches for leaf area index estimation[J]. Remote Sensing, 2023, 15(1): 7. DOI: 10.3390/rs15010007
doi: 10.3390/rs15010007
|
|
|
| [33] |
CHAKHVASHVILI E, BENDIG J, SIEGMANN B, et al. LAI and leaf chlorophyll content retrieval under changing spatial scale using a UAV-mounted multispectral camera[C]//Proceedings of the IGARSS 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur, Malaysia: IEEE, 2022.
|
|
|
| [34] |
MAIMAITIJIANG M, GHULAM A, SIDIKE P, et al. Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 134: 43-58. DOI: 10.1016/j.isprsjprs.2017.10.011
doi: 10.1016/j.isprsjprs.2017.10.011
|
|
|
| [35] |
SARKAR S, CAZENAVE A B, OAKES J, et al. Aerial high-throughput phenotyping of peanut leaf area index and lateral growth[J]. Scientific Reports, 2021, 11: 21661. DOI: 10.1038/s41598-021-00936-w
doi: 10.1038/s41598-021-00936-w
|
|
|
| [36] |
QI H X, ZHU B Y, WU Z Y, et al. Estimation of peanut leaf area index from unmanned aerial vehicle multispectral images[J]. Sensors, 2020, 20(23): 6732. DOI: 10.3390/s20236732
doi: 10.3390/s20236732
|
|
|
| [37] |
PENG Y, ZHU T E, LI Y C, et al. Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications[J]. Agricul-tural and Forest Meteorology, 2019, 271: 116-125. DOI: 10.1016/j.agrformet.2019.02.032
doi: 10.1016/j.agrformet.2019.02.032
|
|
|
| [38] |
QI H X, WU Z Y, ZHANG L, et al. Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction[J]. Computers and Electronics in Agriculture, 2021, 187: 106292. DOI: 10.1016/j.compag.2021.106292
doi: 10.1016/j.compag.2021.106292
|
|
|
| [39] |
刘仕元,梁晋,王帅斌,等.基于无人机遥感的花生叶片叶绿素含量监测研究[J].花生学报,2020,49(2):21-35. DOI:10.14001/j.issn.1002-4093.2020.02.004
LIU S Y, LIANG J, WANG S B, et al. Research on monitoring of peanut leaf chlorophyll content based on UAV remote sensing[J]. Journal of Peanut Science, 2020, 49(2): 21-35. (in Chinese with English abstract)
doi: 10.14001/j.issn.1002-4093.2020.02.004
|
|
|
| [40] |
MAO Z H, DENG L, DUAN F Z, et al. Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 93: 102198. DOI: 10.1016/j.jag.2020.102198
doi: 10.1016/j.jag.2020.102198
|
|
|
| [41] |
HUANG Y Y, MA Q M, WU X M, et al. Estimation of chlorophyll content in Brassica napus based on unmanned aerial vehicle images[J]. Oil Crop Science, 2022, 7(3): 149-155. DOI: 10.1016/j.ocsci.2022.08.004
doi: 10.1016/j.ocsci.2022.08.004
|
|
|
| [42] |
刘源,张飙,杨小平.基于无人机的叶绿素荧光参数检测系统设计[J].扬州大学学报(自然科学版),2014,17(1):38-41. DOI:10.19411/j.1007-824x.2014.01.010
LIU Y, ZHANG B, YANG X P. A design of chlorophyll fluorescence parameters detection system based on unmanned aerial vehicle (UAV)[J]. Journal of Yangzhou University (Natural Science Edition), 2014, 17(1): 38-41. (in Chinese with English abstract)
doi: 10.19411/j.1007-824x.2014.01.010
|
|
|
| [43] |
梁晋,刘仕元,王帅彬,等.基于无人机遥感的花生氮营养反演研究[J].中国油料作物学报,2020,42(6):1043-1050. DOI:10.19802/j.issn.1007-9084.2019234
LIANG J, LIU S Y, WANG S B, et al. Peanut nitrogen nutrition inversion based on unmanned aerial vehicle remote sensing[J]. Chinese Journal of Oil Crop Sciences, 2020, 42(6): 1043-1050. (in Chinese with English abstract)
doi: 10.19802/j.issn.1007-9084.2019234
|
|
|
| [44] |
高开秀,高雯晗,明金,等.无人机载多光谱遥感监测冬油菜氮素营养研究[J].中国油料作物学报,2019,41(2):232-242. DOI:10.7505/j.issn.1007-9084.2019.02.011
GAO K X, GAO W H, MING J, et al. Monitoring of nitrogen nutrition in winter rapeseed using UAV-borne multi-spectral data[J]. Chinese Journal of Oil Crop Sciences, 2019, 41(2): 232-242. (in Chinese with English abstract)
doi: 10.7505/j.issn.1007-9084.2019.02.011
|
|
|
| [45] |
ABDALLA A, CEN H Y, WAN L, et al. Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model[J]. IEEE Transactions on Industrial Infor-matics, 2021, 17(6): 4379-4389. DOI: 10.1109/TII.2020.3009736
doi: 10.1109/TII.2020.3009736
|
|
|
| [46] |
SANTANA D C, TEIXEIRA FILHO M C M, SILVA M R DA, et al. Machine learning in the classification of soybean genotypes for primary macronutrients’ content using UAV-multispectral sensor[J]. Remote Sensing, 2023, 15(5): 1457. DOI: 10.3390/rs15051457
doi: 10.3390/rs15051457
|
|
|
| [47] |
AMIRRUDDIN A D, MUHARAM F M, ISMAIL M H, et al. Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spec-troradiometers and unmanned aerial vehicles[J]. Computers and Electronics in Agriculture, 2022, 193: 106646. DOI: 10.1016/j.compag.2021.106646
doi: 10.1016/j.compag.2021.106646
|
|
|
| [48] |
NOGUERA M, AQUINO A, PONCE J M, et al. Nutritional status assessment of olive crops by means of the analysis and modelling of multispectral images taken with UAVs[J]. Biosystems Engineering, 2021, 211: 1-18. DOI: 10.1016/j.biosystemseng.2021.08.035
doi: 10.1016/j.biosystemseng.2021.08.035
|
|
|
| [49] |
MAIMAITIJIANG M, SAGAN V, SIDIKE P, et al. Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 27-41. DOI: 10.1016/j.isprsjprs.2019.03.003
doi: 10.1016/j.isprsjprs.2019.03.003
|
|
|
| [50] |
MOREIRA F F, DE OLIVEIRA H R, LOPEZ M A, et al. High-throughput phenotyping and random regression models reveal temporal genetic control of soybean biomass production[J]. Frontiers in Plant Science, 2021, 12: 715983. DOI: 10.3389/fpls.2021.715983
doi: 10.3389/fpls.2021.715983
|
|
|
| [51] |
WAN L, ZHANG J F, DONG X Y, et al. Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model[J]. Computers and Electronics in Agriculture, 2021, 187: 106304. DOI: 10.1016/j.compag.2021.106304
doi: 10.1016/j.compag.2021.106304
|
|
|
| [52] |
LIU Y N, LIU S S, LI J, et al. Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images[J]. Computers and Electronics in Agriculture, 2019, 166: 105026. DOI: 10.1016/j.compag.2019.105026
doi: 10.1016/j.compag.2019.105026
|
|
|
| [53] |
GNYP M L, MIAO Y X, YUAN F, et al. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages[J]. Field Crops Research, 2014, 155: 42-55. DOI: 10.1016/j.fcr.2013.09.023
doi: 10.1016/j.fcr.2013.09.023
|
|
|
| [54] |
LUKAS V, HUŇADY I, KINTL A, et al. Using UAV to identify the optimal vegetation index for yield prediction of oil seed rape (Brassica napus L.) at the flowering stage[J]. Remote Sensing, 2022, 14(19): 4953. DOI: 10.3390/rs14194953
doi: 10.3390/rs14194953
|
|
|
| [55] |
MAIMAITIJIANG M, SAGAN V, SIDIKE P, et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning[J]. Remote Sensing of Environment, 2020, 237: 111599. DOI: 10.1016/j.rse.2019.111599
doi: 10.1016/j.rse.2019.111599
|
|
|
| [56] |
SILVA E E DA, BAIO F H R, TEODORO L P R, et al. UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation[J]. Remote Sensing Applications: Society and Environment, 2020, 18: 100318. DOI: 10.1016/j.rsase.2020.100318
doi: 10.1016/j.rsase.2020.100318
|
|
|
| [57] |
ALABI T R, ABEBE A T, CHIGEZA G, et al. Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa[J]. Remote Sensing Applications: Society and Environment, 2022, 27: 100782. DOI: 10.1016/j.rsase.2022.100782
doi: 10.1016/j.rsase.2022.100782
|
|
|
| [58] |
GONG Y, DUAN B, FANG S H, et al. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis[J]. Plant Methods, 2018, 14: 70. DOI: 10.1186/s13007-018-0338-z
doi: 10.1186/s13007-018-0338-z
|
|
|
| [59] |
ORTENZI L, VIOLINO S, PALLOTTINO F, et al. Early estimation of olive production from light drone orthophoto, through canopy radius[J]. Drones, 2021, 5(4): 118. DOI: 10.3390/drones5040118
doi: 10.3390/drones5040118
|
|
|
| [60] |
LI J J, XIE T J, CHEN Y H, et al. High-throughput UAV-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field[J]. Journal of Experimental Botany, 2022, 73(15): 5264-5278. DOI: 10.1093/jxb/erac242
doi: 10.1093/jxb/erac242
|
|
|
| [61] |
安谈洲,李俐俐,张瑞杰,等.无人机遥感及深度学习在油菜冻害识别中的应用研究[J].中国油料作物学报,2021,43(3):479-486. DOI:10.19802/j.issn.1007-9084.2021066
AN T Z, LI L L, ZHANG R J, et al. Application research of unmanned aerial vehicle remote sensing and deep learning in identification of freezing injury in rapeseed[J]. Chinese Journal of Oil Crop Sciences, 2021, 43(3): 479-486. (in Chinese with English abstract)
doi: 10.19802/j.issn.1007-9084.2021066
|
|
|
| [62] |
ZHOU J, ZHOU J F, YE H, et al. Classification of soybean leaf wilting due to drought stress using UAV-based imagery[J]. Computers and Electronics in Agriculture, 2020, 175: 105576. DOI: 10.1016/j.compag.2020.105576
doi: 10.1016/j.compag.2020.105576
|
|
|
| [63] |
ZHOU J, MOU H W, ZHOU J F, et al. Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning[J]. Plant Phenomics, 2021, 2021: 9892570. DOI: 10.34133/2021/9892570
doi: 10.34133/2021/9892570
|
|
|
| [64] |
CHEN T T, YANG W G, ZHANG H J, et al. Early detection of bacterial wilt in peanut plants through leaf-level hyper-spectral and unmanned aerial vehicle data[J]. Computers and Electronics in Agriculture, 2020, 177: 105708. DOI: 10.1016/j.compag.2020.105708
doi: 10.1016/j.compag.2020.105708
|
|
|
| [65] |
SHAHI T B, XU C Y, NEUPANE A, et al. A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images[J]. PLoS ONE, 2023, 18(3): e0282486. DOI: 10.1371/journal.pone.0282486
doi: 10.1371/journal.pone.0282486
|
|
|
| [66] |
HATTON N M, MENKE E, SHARDA A, et al. Assessment of sudden death syndrome in soybean through multispectral broadband remote sensing aboard small unmanned aerial systems[J]. Computers and Electronics in Agriculture, 2019, 167: 105094. DOI: 10.1016/j.compag.2019.105094
doi: 10.1016/j.compag.2019.105094
|
|
|
| [67] |
KURIHARA J, KOO V C, GUEY C W, et al. Early detection of basal stem rot disease in oil palm tree using unmanned aerial vehicle-based hyperspectral imaging[J]. Remote Sensing, 2022, 14(3): 799. DOI: 10.3390/rs14030799
doi: 10.3390/rs14030799
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