Advances in Crop Nitrogen Diagnosis Based on UAV Remote Sensing

doi:10.11924/j.issn.1000-6850.casb2025-0090

Abstract

Abstract:

This paper focuses on the application of UAV remote sensing in crop nitrogen diagnosis, and comprehensively summarizes the development of nitrogen diagnosis technology from traditional nitrogen diagnosis technology, diagnosis technology based on digital image analysis to diagnosis technology of UAV remote sensing. The research and application progress of UAV remote sensing technology in nitrogen diagnosis of various crops are deeply analyzed. It is pointed out that the technology has the advantages of strong mobility, high degree of automation, non-destructive and high efficiency in precision agriculture. At the same time, the current challenges of the technology are objectively analyzed, such as massive data processing, limited model generalization ability, high application cost, and vulnerability to environmental interference. Finally, the future directions of deepening the mechanism and innovation model, breaking through the bottleneck of core technology, building intelligent application ecology, and promoting standardization and scale are prospected, aiming to provide theoretical support for promoting the in-depth application of UAV remote sensing technology in the field of precision agriculture.

Key words: UAV remote sensing, nitrogen diagnosis, digital image analysis, precision agriculture, non-destructive and efficient, data fusion

HAN Yanlu, ZHU Yi, YIN Yilu, WANG Huizheng, LAN Yubin, ZHAO Shuo. Advances in Crop Nitrogen Diagnosis Based on UAV Remote Sensing[J]. Chinese Agricultural Science Bulletin, 2025, 41(24): 126-134.

Figures/Tables 1

References 80

[1]	RAY S, HAQIQI I, HILL A E, et al. Labor markets: a critical link between global-local shocks and their impact on agriculture[J]. Environmental research letters, 2023, 18(3):35007.
[2]	HAIKUAN F, HUILIN T, ZHENHAI L, et al. Comparison of UAV RGB imagery and hyperspectral remote-sensing data for monitoring winter wheat growth[J]. Remote sensing, 2022, 14(15):3811.
[3]	ZHU H, LIN C, LIU G, et al. Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection[J]. Frontiers in plant science, 2024,15:1435016.
[4]	谭雨蕾, 李雪岩, 张力元. 高光谱遥感在农作物研究中的应用与发展趋势[J]. 中国农学通报, 2024, 40(34):141-148. doi: 10.11924/j.issn.1000-6850.casb2024-0097
[5]	何勇, 岑海燕, 何立文, 等. 农用无人机技术及其应用[J]. 浙江大学学报(农业与生命科学版), 2018, 44(4):489.
[6]	孟凡彬, 张云鹏. 无人机技术在农业领域的应用及展望[J]. 时代农机, 2019, 46(2):49-50.
[7]	杨文云. 无人机技术在农业生产中的应用[J]. 农业灾害研究, 2018, 8(6):17-18.
[8]	郝雅洁, 史维杰, 赵明霞, 等. 论无人机技术在农业领域的应用[J]. 物联网技术, 2019, 9(9):59-62.
[9]	陈治钢, 廖桂平. 无人机高光谱遥感在作物表型监测中的应用进展[J]. 作物研究, 2024, 38(6):525-530.
[10]	BLEKANOV I, MOLIN A, ZHANG D, et al. Monitoring of grain crops nitrogen status from uav multispectral images coupled with deep learning approaches[J]. Computers and electronics in agriculture, 2023, 212:108047.
[11]	ZHANG J, HU Y, LI F, et al. Meta-analysis assessing potential of drone remote sensing in estimating plant traits related to nitrogen use efficiency[J]. Remote sensing, 2024, 16(5):838.
[12]	宋小云. 凯氏法测定土壤全氮的方法改进[J]. 环境与发展, 2019, 31(8):120-121.
[13]	刘小芳, 张泽, 吕新, 等. 基于硝酸盐的滴灌春小麦氮素追肥模型建立研究[J]. 新疆农业科学, 2017, 54(1):27-32.
[14]	王蕊, 史自航, 董冰, 等. 硝酸盐快速诊断技术在氮素营养诊断中的应用分析[J]. 中国农业大学学报, 2013, 18(3):81-90.
[15]	韩宁, 史普想, 金振, 等. 氮素营养诊断技术的发展及其在花生上的应用[J]. 辽宁农业科学, 2022(1):50-55.
[16]	陈魏涛, 曹宏鑫, 张保军, 等. 氮素营养诊断技术及其在油菜上的应用研究进展[J]. 江苏农业学报, 2016, 32(4):953-960.
[17]	李悦书. 关于作物氮素营养诊断方法的研究[J]. 山西农经, 2015(7):67.
[18]	孙凯旋, 张玉屏, 向镜, 等. 水稻氮素营养诊断及施用策略研究进展[J]. 杂交水稻, 2024, 39(2):1-7.
[19]	李鹏程, 郑苍松, 孙淼, 等. 国内棉花氮营养诊断和推荐施氮研究进展[J]. 中国棉花, 2019, 46(6):1-4,15. doi: 10.11963/1000-632X.lpcdhl.20190611
[20]	王磊, 卢艳丽, 白由路. 主要粮食作物基于SPAD的氮素营养诊断方法研究进展[J]. 植物营养与肥料学报, 2022, 28(3):546-554.
[21]	ZHANG Z, XU X, JIN M, et al. A new curve of critical leaf nitrogen concentration based on the maximum root dry matter for diagnosing nitrogen nutritional status of sweetpotato[J]. European journal of agronomy, 2024,156:127176.
[22]	XIN Q, YANAN Z, YUFANG H, et al. A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram[J]. Scientific reports, 2021, 11(1):13012. doi: 10.1038/s41598-021-92431-5 pmid: 34155294
[23]	LI D, ZHANG P, CHEN T, et al. Recent development and challenges in spectroscopy and machine vision technologies for crop nitrogen diagnosis: a review[J]. Remote sensing, 2020, 12(16):2578.
[24]	齐欣, 汪洋, 黄玉芳, 等. 基于直方图的手机玉米冠层数字图像氮素诊断方法[J]. 中国农业科学, 2024, 57(20):4094-4106. doi: 10.3864/j.issn.0578-1752.2024.20.014
[25]	李紫琴, 王家强, 李贞, 等. 基于光谱指数的棉花叶片叶绿素密度估算研究[J]. 中国农业科技导报, 2024, 26(8):103-111.
[26]	李红宇, 高正武, 王志君, 等. 基于光谱反射率的寒地水稻叶片氮含量预测[J]. 光谱学与光谱分析, 2024, 44(9):2582-2593.
[27]	魏全全, 芶久兰, 李飞, 等. 基于无人机可见光遥感的马铃薯植株氮素累积估测与验证[J]. 核农学报, 2024, 38(10):2003-2010. doi: 10.11869/j.issn.1000-8551.2024.10.2003
[28]	LI D L, YANG S, DU Z Z, et al. Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: a systematic literature review[J]. Computers and electronics in agriculture, 2024,227:109565.
[29]	黄培峰, 朱立学, 陈家政. 农业无人机遥感技术研究进展[J]. 机电产品开发与创新, 2021, 34(3):43-45.
[30]	ZHANG H D, WANG L Q, TIAN T, et al. A review of unmanned aerial vehicle low-altitude remote sensing (UAV-LARS) use in agricultural monitoring in China[J]. Remote sensing, 2021, 13(6):1221.
[31]	JEONG S, KO J, KIM M, et al. Construction of an unmanned aerial vehicle remote sensing system for crop monitoring[J]. Journal of applied remote sensing, 2016, 10(2):026027.
[32]	樊湘鹏, 周建平, 许燕. 无人机低空遥感监测农情信息研究进展[J]. 新疆大学学报(自然科学版)(中英文), 2021, 38(5):623-631.
[33]	HUANG Y, LI D, LIU X, et al. Monitoring canopy SPAD based on UAV and multispectral imaging over fruit tree growth stages and species[J]. Frontiers in plant science, 2024,15:1435613.
[34]	MIA M S, TANABE R, HABIBI L N, et al. Multimodal deep learning for rice yield prediction using UAV-based multispectral imagery and weather data[J]. Remote sensing, 2023, 15(10):2511.
[35]	HASSLER S C, BAYSAL-GUREL F. Unmanned aircraft system (UAS) technology and applications in agriculture[J]. Agronomy, 2019, 9(10):618.
[36]	ABDELMALEK B, HAFED Z, AHMED K, et al. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images[J]. Cluster computing, 2022, 26(2):1297-1317.
[37]	NADIA D, CENGIZ K, JOHN N, et al. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges[J]. Remote sensing, 2021, 13(6):1204.
[38]	DENG L, MAO Z H, LI X J, et al. UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras[J]. Isprs Journal of photogrammetry and remote Sensing, 2018,146:124-136.
[39]	LUO S J, JIANG X Q, YANG K L, et al. Multispectral remote sensing for accurate acquisition of rice phenotypes: impacts of radiometric calibration and unmanned aerial vehicle flying altitudes[J]. Frontiers in plant science, 2022,13:958106.
[40]	SHAHI T B, XU C Y, NEUPANE A, et al. Recent advances in crop disease detection using UAV and deep learning techniques[J]. Remote sensing, 2023, 15(9):2450.
[41]	YAO H, QIN R J, CHEN X Y. Unmanned aerial vehicle for remote sensing applications-a review[J]. Remote sensing, 2019, 11(12):1443.
[42]	LU B, DAO P D, LIU J G, et al. Recent advances of hyperspectral imaging technology and applications in agriculture[J]. Remote sensing, 2020, 12(16):2659.
[43]	张智韬, 边江, 韩文霆, 等. 无人机热红外图像计算冠层温度特征数诊断棉花水分胁迫[J]. 农业工程学报, 2018, 34(15):77-84.
[44]	MESSINA G, MODICA G. Applications of UAV thermal imagery in precision agriculture: state of the art and future research outlook[J]. Remote sensing, 2020, 12(9):1491.
[45]	GARCíA-TEJERO I F, ORTEGA-ARéVALO C J, IGLESIAS-CONTRERAS M, et al. Assessing the crop-water status in almond (Prunus dulcis Mill.) trees via thermal imaging camera connected to smartphone[J]. Sensors, 2018, 18(4):1050.
[46]	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.
[47]	JIMENEZ-BERNI J A, DEERY D M, ROZAS-LARRAONDO P, et al. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR[J]. Frontiers in plant science, 2018,9:237.
[48]	DEBNATH D, VANEGAS F, SANDINO J, et al. A review of UAV path-planning algorithms and obstacle avoidance methods for remote sensing applications[J]. Remote sensing, 2024, 16(21):4019.
[49]	YANG W, FU H, XU W, et al. Optimizing data consistency in UAV multispectral imaging for radiometric correction and sensor conversion models[J]. Remote sensing, 2025, 17(12):2001.
[50]	HAN Y, ZHANG J, BAI Y, et al. Ensemble learning-driven and UAV multispectral analysis for estimating the leaf nitrogen content in winter wheat[J]. Agronomy, 2025, 15(7):1621.
[51]	HU T, LIU Z, HU R, et al. Convolutional neural network-based estimation of nitrogen content in regenerating rice leaves[J]. Agronomy, 2024, 14(7):1422.
[52]	刘涛, 张寰, 王志业, 等. 利用无人机多光谱估算小麦叶面积指数和叶绿素含量[J]. 农业工程学报, 2021, 37(19):65-72.
[53]	陶新宇, 朱永基, 苏祥祥, 等. 基于无人机多光谱影像的冬小麦花前期氮素营养估测[J]. 安徽科技学院学报, 2023, 37(3):50-59.
[54]	李冰, 刘镕源, 刘素红, 等. 基于低空无人机遥感的冬小麦覆盖度变化监测[J]. 农业工程学报, 2012, 28(13):6.
[55]	刘建立, 李晓鹏. “基于无人机低空高精度遥感的冬小麦和夏玉米变量施肥管理模型”进展[J]. 浙江大学学报(农业与生命科学版), 2018, 44(4):413.
[56]	GUO B B, FENG Y L, MA C, et al. Suitability of different multivariate analysis methods for monitoring leaf N accumulation in winter wheat using in situ hyperspectral data[J]. Computers and electronics in agriculture, 2022,198:107115.
[57]	张星宇, 张月, 夏晨真, 等. 基于无人机高光谱影像的玉米叶片氮素含量估算[J]. 遥感技术与应用, 2024, 39(4):927-939. doi: 10.11873/j.issn.1004-0323.2024.4.0927
[58]	张玲, 陈新平, 贾良良. 基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J]. 植物营养与肥料学报, 2018, 24(1):261-269.
[59]	WANG Y, FENG C H, MA Y R, et al. Estimation of nitrogen concentration in walnut canopies in Southern Xinjiang based on UAV multispectral images[J]. Agronomy-basel, 2023, 13(6):1604.
[60]	郝琪, 陈天陆, 王富贵, 等. 基于无人机多光谱数据和氮素空间分异的玉米冠层氮浓度估算[J]. 作物学报, 2025, 51(1):189-206. doi: 10.3724/SP.J.1006.2025.43015
[61]	YU X J, HUO X F, QIAN L, et al. Combining UAV multispectral and thermal infrared data for maize growth parameter estimation[J]. Agriculture-basel, 2024, 14(11):2004.
[62]	许童羽, 白驹驰, 郭忠辉, 等. 基于无人机高光谱遥感的水稻氮营养诊断方法[J]. 农业机械学报, 2023, 54(2):189-197.
[63]	YU F H, BAI J C, JIN Z Y, et al. Estimating the rice nitrogen nutrition index based on hyperspectral transform technology[J]. Frontiers in plant science, 2023,14:1118098.
[64]	傅友强, 钟旭华, 黄农荣, 等. 基于无人机多光谱遥感的水稻冠层光谱特征和氮素营养关系研究[J]. 广东农业科学, 2021, 48(10):121-131.
[65]	FAN Y G, FENG H K, JIN X L, et al. Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices[J]. Frontiers in plant science, 2022, 13:1012070.
[66]	LI D, MIAO Y X, GUPTA S K, et al. Improving potato yield prediction by combining cultivar information and UAV remote sensing data using machine learning[J]. Remote sensing, 2021, 13(16):3322.
[67]	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):21-27,35.
[68]	SISHODIA R P, RAY R L, SINGH S K. Applications of remote sensing in precision agriculture: a review[J]. Remote sensing, 2020, 12(19):3136.
[69]	JEONG M, PARK S, KWON S M, et al. Rapid detection of soybean nutrient deficiencies with YOLOv8s for precision agriculture advancement[J]. Scientific reports, 2025, 15(1):13810.
[70]	向友珍, 安嘉琪, 赵笑, 等. 基于无人机多光谱遥感的大豆生长参数和产量估算[J]. 农业机械学报, 2023, 54(8):230-239.
[71]	石浩磊, 曹红霞, 张伟杰, 等. 基于无人机多光谱的棉花多生育期叶面积指数反演[J]. 中国农业科学, 2024, 57(1):80-95. doi: 10.3864/j.issn.0578-1752.2024.01.007
[72]	LIU S Y, ZHANG B, YANG W G, et al. Quantification of physiological parameters of rice varieties based on multi-spectral remote sensing and machine learning models[J]. Remote sensing, 2023, 15(2):453.
[73]	LI M H, LIU Y, LU X, et al. Integrating unmanned aerial vehicle-derived vegetation and texture indices for the estimation of leaf nitrogen concentration in drip-irrigated cotton under reduced nitrogen treatment and different plant densities[J]. Agronomy-basel, 2024, 14(1):120.
[74]	CHIN R, CATAL C, KASSAHUN A. Plant disease detection using drones in precision agriculture[J]. Precision agriculture, 2023, 24(5):1663-1682.
[75]	唐子竣, 向友珍, 王辛, 等. 利用相关矩阵法优化光谱指数的冬油菜氮素营养诊断[J]. 农业工程学报, 2023, 39(17):97-106.
[76]	王晗, 向友珍, 李汪洋, 等. 基于无人机多光谱遥感的冬油菜地上部生物量估算[J]. 农业机械学报, 2023, 54(8):218-229.
[77]	SHISHI L, XIAOHUI B, GEGE Z, et al. Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect[J]. Remote sensing of environment, 2023, 284:113348.
[78]	PAUT R, MORISON M V, RAYON B, et al. Critical nitrogen dilution curves for winter oilseed rape (Brassica napus L.) along the whole crop cycle: a bayesian analysis[J]. European journal of agronomy, 2025,168:127642.
[79]	LIU S, LI L, FAN H, et al. Real-time and multi-stage recommendations for nitrogen fertilizer topdressing rates in winter oilseed rape based on canopy hyperspectral data[J]. Industrial crops & products, 2020,154:112699.
[80]	魏勇. 基于无人机载高光谱雷达一体化成像的九叶青花椒树体氮营养无损诊断研究[D]. 重庆: 西南大学, 2024.

诊断方法			原理	优点	缺点	参考文献
传统诊断技术	化学诊断法	植株全氮诊断法	测定植株地上部分或特定部位的全氮含量，与作物不同生长阶段的氮素含量标准值对比	结果准确	操作复杂	[12]
		植株硝酸盐诊断法	氮素充足时作物吸收的硝态氮会在体内积累，通过测定植株特定部位硝酸盐含量判断氮素营养状况	操作简便快速	准确性受环境和生长阶段影响	[13-14]
		土壤诊断法	采集具有代表性的土壤样品进行测定	能反映土壤氮素基础供应能力	需结合植株氮素诊断结果综合分析，土壤氮素有效性受多种因素制约	[15]
	外观诊断法		通过观察作物植株特定的外观特征来判断其可能缺乏的营养元素	可直观判断作物氮素营养情况	肉眼判断不精准，且受品种、植株密度、土壤营养状况及人为主观判断影响	[16-18]
	肥料窗口法		在试验田抽样区域跟踪监测作物氮素吸收程度和含量，判断整体氮素状况	操作简单，在平原地带实用性高	若缺氮区域分布不均，所选区域避开缺氮区会造成判断失误	[15,17]
	叶绿素仪法		通过测定作物叶片叶绿素含量相关指标（如SPAD值）来判断整体氮素状况	简便，时效性强，能一定程度反映作物氮素营养状况	实际应用中受作物的品种、生育期、生长环境的影响	[19-20]
基于可见光图像的诊断技术			以光谱学原理为依据，分析作物叶片光谱参数与氮营养指标的相关性	无损诊断	受图像获取时间等因素影响，存在局限性	[16,19]
基于其他光谱遥感的诊断技术			依据绿色植被指数、光谱反射特性与叶片氮积累量的相关性	无损、高效、可大面积监测作物氮素营养状况	数据处理复杂，结果一致性欠佳	[25-27]

诊断方法			原理	优点	缺点	参考文献
传统诊断技术	化学诊断法	植株全氮诊断法	测定植株地上部分或特定部位的全氮含量，与作物不同生长阶段的氮素含量标准值对比	结果准确	操作复杂	[12]
		植株硝酸盐诊断法	氮素充足时作物吸收的硝态氮会在体内积累，通过测定植株特定部位硝酸盐含量判断氮素营养状况	操作简便快速	准确性受环境和生长阶段影响	[13-14]
		土壤诊断法	采集具有代表性的土壤样品进行测定	能反映土壤氮素基础供应能力	需结合植株氮素诊断结果综合分析，土壤氮素有效性受多种因素制约	[15]
	外观诊断法		通过观察作物植株特定的外观特征来判断其可能缺乏的营养元素	可直观判断作物氮素营养情况	肉眼判断不精准，且受品种、植株密度、土壤营养状况及人为主观判断影响	[16-18]
	肥料窗口法		在试验田抽样区域跟踪监测作物氮素吸收程度和含量，判断整体氮素状况	操作简单，在平原地带实用性高	若缺氮区域分布不均，所选区域避开缺氮区会造成判断失误	[15,17]
	叶绿素仪法		通过测定作物叶片叶绿素含量相关指标（如SPAD值）来判断整体氮素状况	简便，时效性强，能一定程度反映作物氮素营养状况	实际应用中受作物的品种、生育期、生长环境的影响	[19-20]
基于可见光图像的诊断技术			以光谱学原理为依据，分析作物叶片光谱参数与氮营养指标的相关性	无损诊断	受图像获取时间等因素影响，存在局限性	[16,19]
基于其他光谱遥感的诊断技术			依据绿色植被指数、光谱反射特性与叶片氮积累量的相关性	无损、高效、可大面积监测作物氮素营养状况	数据处理复杂，结果一致性欠佳	[25-27]