Remote Sensing for Rice Growth Stages Monitoring: Research Progress, Bottleneck Problems and Technical Optimization Paths

doi:10.12133/j.smartag.SA202412019

Abstract

Abstract:

[Significance] The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement. Remote sensing, characterized by high spatial-temporal resolution and automated monitoring capabilities, provides transformative solutions for large-scale dynamic phenology monitoring, offering essential technical support to address climate change impacts and food security challenges in complex agroecosystems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management. [Progress] In recent years, the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the perception layer, decision-making layer, and execution layer, forming a technological ecosystem covering the entire chain of "data acquisition-feature analysis-intelligent decision-making-precise operation". At the perception layer, a "space-air-ground" three-dimensional monitoring network has been constructed: high-altitude satellites (Sentinel-2, Landsat) realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations; low-altitude unmanned aerial vehicle (UAVs) equipped with hyperspectral and light detection and ranging (LiDAR) sensors analyze the heterogeneity of canopy three-dimensional structure; near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters. Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data, forming continuous and stable monitoring capabilities. Innovations in technical methods show three integration trends: first, multimodal data collaboration mechanisms break through the physical characteristic barriers between optical and radar data. Second, deep integration of mechanistic models and data-driven approaches embeds the PROpriétés SPECTrales-Scattering by arbitrary inclined leaves (PROSAIL) radiative transfer model into the long short-term memory (LSTM) network architecture. Third, cross-scale feature analysis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms, realizing multi-granularity mapping between panicle texture features and canopy leaf area index (LAI) fluctuations. The current technical system has completed three-dimensional leaps: From discrete manual observations to full-cycle continuous perception, with monitoring frequency upgraded from weekly to hourly; from empirical threshold-based judgment to mechanism-data hybrid-driven, the cross-regional generalization ability of the model can be significantly improved; from independent link operations to full-chain collaboration of "perception-decision-execution", constructing a digital management closed-loop covering rice sowing to harvest, providing core technical support for smart farm construction. [Conclusions and Prospects] Current technologies face three-tiered challenges: Data heterogeneity, feature limitations and algorithmic constraints. Future research should focus on three aspects: 1) Multi-source data assimilation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminated data reconstruction; 2) Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted indices to overcome spectral saturation; 3) Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation, developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection, and implementing transfer learning for cross-regional model generalization. The convergence of multi-source remote sensing, intelligent algorithms, and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.

Key words: rice, growth period, remote sensing, models, deep learning, multi-source fusion

CLC Number:

S126

LI Ruijie, WANG Aidong, WU Huaxing, LI Ziqiu, FENG Xiangqian, HONG Weiyuan, TANG Xuejun, QIN Jinhua, WANG Danying, CHU Guang, ZHANG Yunbo, CHEN Song. Remote Sensing for Rice Growth Stages Monitoring: Research Progress, Bottleneck Problems and Technical Optimization Paths[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202412019.

Figures/Tables 9

Fig. 1

Fig. 2

Table 1

Fig. 3

Table 2

Quantitative reproductive period indicators of some remote sensing inversion indicators

量化指标	原理	低值期	上升期	峰值期	下降期	末期
NDVI ^{［ 27］}	$N D V I = (N I R - R e d) (N I R + R e d)$	0.1~0.2	0.3~0.6	0.6~0.8	0.5~0.7	0.3~0.5
EVI ^{［ 28］}	$E V I = G ∙ (N I R - R e d) N I R + C 1 ∙ R e d - C 2 ∙ B l u e + L$	0.1~0.3	0.4~0.7	0.7~0.9	0.6~0.8	0.4~0.6
LAI ^{［ 29］}	$L A I = 单位叶面积地面面积$	0.1~0.5	1.0~2.5	2.5~4.0	3.0~5.0	2.0~3.0
Biomass ^{［ 30］}	生物量通过干重测量/ （t/ha）	0.1~0.5	1.0~2.0	2.0~4.0	3.0~6.0	5.0~10.0
CNC ^{［ 31］}	通过测定植株氮元素含量/%	2~3	3~4	2~3	1~2	1.0~1.5
CIred edge ^{［ 28］}	$C I r e d e d g e = N I R R e d E d g e - 1$	1~2	2~3	3~4	2~3	1~2

Table 2

Fig. 4

Fig. 5

Table 3

Fig. 6

References 118

1	Food and Agriculture Organization of the United Nations (FAO), International Fund for Agricultural Development (IFAD), United Nations Children's Fund (UNICEF), et al. The state of food security and nutrition in the world 2024: Financing to end hunger, food insecurity and malnutrition in all its forms[R]. Rome: FAO, 2024.
2	MOLDENHAUER K, SLATON N. Rice Growth and Development[M]// University of Arkansas Division of Agriculture. Rice Production Handbook. Fayetteville, AR: University of Arkansas Cooperative Extension Service, 2001: 7- 14.
3	XU F X, ZHANG L, ZHOU X B, et al. The ratoon rice system with high yield and high efficiency in China: Progress, trend of theory and technology[J]. Field crops research, 2021, 272: ID 108282.
4	郎有忠, 窦永秀, 王美娥, 等. 水稻生育期对籽粒产量及品质的影响[J]. 作物学报, 2012, 38( 3): 528- 534.
	LANG Y Z, DOU Y X, WANG M E, et al. Effects of growth duration on grain yield and quality in rice ( Oryza sativa L.)[J]. Acta agronomica sinica, 2012, 38( 3): 528- 534.
5	WANG S H, CHEN W X, DONG J, et al. Physiological characteristics and high-yield techniques with SRI rice[C]// Assessments of the System of Rice Intensification: Proceedings of an International Conference. Ithaca, NY, USA: Cornell International Institute for Food, Agriculture and Development, 2002: 116- 124.
6	XUE H, XU X, ZHU Q, et al. Rice yield and quality estimation coupling hierarchical linear model with remote Sensing[J]. Computers and electronics in agriculture, 2024, 218: ID 108731.
7	YANG Q, SHI L S, HAN J Y, et al. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images[J]. Field crops research, 2019, 235: 142- 153.
8	TANG L, ZHU Y, HANNAWAY D, et al. RiceGrow: A rice growth and productivity model[J]. NJAS - wageningen journal of life sciences, 2009, 57( 1): 83- 92.
9	LI T, ANGELES O, MARCAIDA M, et al. From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments[J]. Agricultural and forest meteorology, 2017, 237: 246- 256.
10	LIU X J, WU X T, PENG Y, et al. Application of UAV-retrieved canopy spectra for remote evaluation of rice full heading date[J]. Science of remote sensing, 2023, 7: ID 100090.
11	JIN X L, KUMAR L, LI Z H, et al. A review of data assimilation of remote sensing and crop models[J]. European journal of agronomy, 2018, 92: 141- 152.
12	于铁龙, 杨兰, 张婷婷, 等. 水稻全生育期近地空农情遥感精准监测应用[J]. 中国农业文摘-农业工程, 2022, 34( 6): 55- 64.
	YU T L, YANG L, ZHANG T T, et al. Application of remote sensing to precise monitoring of near-earth air agricultural situation in the whole growth period of rice[J]. Agricultural science and engineering in China, 2022, 34( 6): 55- 64.
13	赵小敏, 孙小香, 王芳东, 等. 水稻高光谱遥感监测研究综述[J]. 江西农业大学学报, 2019, 41( 1): 1- 12.
	ZHAO X M, SUN X X, WANG F D, et al. A summary of the researches on hyperspectral remote sensing monitoring of rice[J]. Acta agriculturae universitatis jiangxiensis, 2019, 41( 1): 1- 12.
14	王帝, 孙榕, 苏勇, 等. 基于无人机多光谱影像的水稻生物量估测[J]. 农业工程学报, 2024, 40( 17): 161- 170.
	WANG D, SUN R, SU Y, et al. Rice biomass estimation based on multispectral imagery from unmanned aerial vehicles[J]. Transactions of the Chinese society of agricultural engineering, 2024, 40( 17): 161- 170.
15	纪景纯, 赵原, 邹晓娟, 等. 无人机遥感在农田信息监测中的应用进展[J]. 土壤学报, 2019, 56( 4): 773- 784.
	JI J C, ZHAO Y, ZOU X J, et al. Advancement in application of UAV remote sensing to monitoring of farmlands[J]. Acta pedologica sinica, 2019, 56( 4): 773- 784.
16	王岩, 高美琦, 李荣平, 等. Sentinel-2遥感影像在盘锦水稻米质监测中的应用研究[J]. 中国稻米, 2024, 30( 6): 74- 81.
	WANG Y, GAO M Q, LI R P, et al. Application of sentinel-2 remote sensing image in rice quality monitoring in Panjin City[J]. China rice, 2024, 30( 6): 74- 81.
17	杨浩, 黄文江, 王纪华, 等. 基于HJ-1A/1BCCD时间序列影像的水稻生育期监测[J]. 农业工程学报, 2011, 27( 4): 219- 224.
	YANG H, HUANG W J, WANG J H, et al. Monitoring rice growth stages based on time series HJ-1A/1B CCD images[J]. Transactions of the Chinese society of agricultural engineering, 2011, 27( 4): 219- 224.
18	JIMENEZ-SIERRA D A, CORREA E S, BENÍTEZ-RESTREPO H D, et al. Novel feature-extraction methods for the estimation of above-ground biomass in rice crops[J]. Sensors, 2021, 21( 13): ID 4369.
19	NAMAZI F, EZOJI M, PARMEHR E G. Paddy Rice mapping in fragmented lands by improved phenology curve and correlation measurements on Sentinel-2 imagery in Google earth engine[J]. Environmental monitoring and assessment, 2023, 195( 10): ID 1220.
20	WANG J, HUANG J F, WANG X Z, et al. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images[J]. Journal of Zhejiang university-SCIENCE B, 2015, 16( 10): 832- 844.
21	YAMASHITA M, KAIEDA T, TOYODA H, et al. Spatial estimation of daily growth biomass in paddy rice field using canopy photosynthesis model based on ground and UAV observations[J]. Remote sensing, 2024, 16( 1): ID 125.
22	BOSCHETTI M, STROPPIANA D, BRIVIO P A, et al. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images[J]. International journal of remote sensing, 2009, 30( 18): 4643- 4662.
23	MA Y, JIANG Q, WU X T, et al. Monitoring hybrid rice phenology at initial heading stage based on low-altitude remote sensing data[J]. Remote sensing, 2021, 13( 1): ID 86.
24	MEHMOOD V, MALIK A I, ZAFAR Z, et al. Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis[C]// Image and Signal Processing for Remote Sensing XXIX. Amsterdam, Netherlands: SPIE, 2023: ID 28.
25	ZHANG X L, ZHANG F, QI Y X, et al. New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)[J]. International journal of applied earth observation and geoinformation, 2019, 78: 215- 226.
26	PRABHAKAR M, GOPINATH K A, RAVI KUMAR N, et al. Mapping leaf area index at various rice growth stages in southern India using airborne hyperspectral remote sensing[J]. Remote sensing, 2024, 16( 6): ID 954.
27	PENG D L, HUETE A R, HUANG J F, et al. Detection and estimation of mixed paddy rice cropping patterns with MODIS data[J]. International journal of applied earth observation and geoinformation, 2011, 13( 1): 13- 23.
28	YANG K L, MO J C, LUO S J, et al. Estimation of rice aboveground biomass by UAV imagery with photosynthetic accumulation models[J]. Plant phenomics, 2023, 5: ID 56.
29	LU B R, CAI X X, XIN J. Efficient indica and Japonica rice identification based on the InDel molecular method: Its implication in rice breeding and evolutionary research[J]. Progress in natural science, 2009, 19( 10): 1241- 1252.
30	XU A N, WANG F, LI L. Vegetation information extraction in karst area based on UAV remote sensing in visible light band[J]. Optik, 2023, 272: ID 170355.
31	ZHANG W Y, CHEN Y J, WANG Z Q, et al. Polyamines and ethylene in rice young panicles in response to soil drought during panicle differentiation[J]. Plant growth regulation, 2017, 82( 3): 491- 503.
32	HUETE A R, LIU H Q, BATCHILY K, et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS[J]. Remote sensing of environment, 1997, 59( 3): 440- 451.
33	LOPRESTI M F, DI BELLA C M, DEGIOANNI A J. Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina [J]. Information processing in agriculture, 2015, 2( 2): 73- 84.
34	HU Y Q, WU Y, TANTIAN Z Z, et al. Capturing urban green view with mobile crowd sensing[J]. Ecological informatics, 2024, 81: ID 102640.
35	CLARK M L, ROBERTS D A, EWEL J J, et al. Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors[J]. Remote sensing of environment, 2011, 115( 11): 2931- 2942.
36	YANG W N, XU X C, DUAN L F, et al. High-throughput measurement of rice tillers using a conveyor equipped with X-ray computed tomography[J]. The review of scientific instruments, 2011, 82( 2): ID 025102.
37	WANG C F, YANG C H, ZHANG J, et al. A PROSAIL model with a vegetation index lookup table optimized with in situ statistics for rapeseed leaf area index estimation using diverse unmanned aerial vehicle sensors in the Yangtze River Basin[J]. Computers and electronics in agriculture, 2023, 215: ID 108418.
38	牛亚晓, 张立元, 韩文霆, 等. 基于无人机遥感与植被指数的冬小麦覆盖度提取方法[J]. 农业机械学报, 2018, 49( 4): 212- 221.
	NIU Y X, ZHANG L Y, HAN W T, et al. Fractional vegetation cover extraction method of winter wheat based on UAV remote sensing and vegetation index [J]. Transactions of the Chinese society for agricultural machinery, 2018, 49( 4): 212- 221.
39	WU T Z, ZHANG Z W, WANG Q, et al. Estimating rice leaf area index at multiple growth stages with Sentinel-2 data: An evaluation of different retrieval algorithms[J]. European journal of agronomy, 2024, 161: ID 127362.
40	ROSLE R, CHE'YA N, ROSLIN N, et al. Monitoring early stage of rice crops growth using normalized difference vegetation index generated from UAV[J]. IOP conference series: Earth and environmental science, 2019, 355( 1): ID 012066.
41	BHATTI M T, GILANI H, ASHRAF M, et al. Field validation of NDVI to identify crop phenological signatures[J]. Precision agriculture, 2024, 25( 5): 2245- 2270.
42	ZHANG W, CHAI Y, SUN Y, et al. Dynamic relationships between rice yield and population indices across growth stages[J]. Agronomy journal, 2015, 107( 3): 1021- 1030.
43	ZHANG Z Y, LU L, ZHAO Y H, et al. Recent advances in using Chinese Earth observation satellites for remote sensing of vegetation[J]. ISPRS journal of photogrammetry and remote sensing, 2023, 195: 393- 407.
44	杨振忠, 方圣辉, 彭漪, 等. 基于机器学习结合植被指数阈值的水稻关键生育期识别[J]. 中国农业大学学报, 2020, 25( 1): 76- 85.
	YANG Z Z, FANG S H, PENG Y, et al. Recognition of the rice growth stage by machine learning combined with vegetation index threshold[J]. Journal of China agricultural university, 2020, 25( 1): 76- 85.
45	LESTARI A I, KUSHARDONO D. The use of c-band synthetic aperture radar satellite data for rice plant growth phase identification[J]. International journal of remote sensing and earth sciences (IJReSES), 2019, 16( 1): ID 31.
46	ZHOU Z G, ZHAO L L, SHI H T, et al. Early season mapping of rice using of time series sentinel-1 SAR images[C]// 2024 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, New Jersey, USA: IEEE, 2024: 4832- 4835.
47	TRISASONGKO B H, PANUJU D R, GRIFFIN A L, et al. Examining the outcome of coupling machine learning with dual polarimetric SAR for rice growth mapping[M]// Agriculture, Livestock Production and Aquaculture. Cham: Springer International Publishing, 2022: 115- 129.
48	SHAO Y, LI K, BRISCO B, et al. The potential of polarimetric and compact SAR data in rice identification[J]. IOP conference series: Earth and environmental science, 2014, 17( 1): ID 012056.
49	DIN M, ZHENG W, RASHID M, et al. Evaluating hyperspectral vegetation indices for leaf area index estimation of Oryza sativa L. at diverse phenological stages[J]. Frontiers in plant science, 2017, 8: ID 820.
50	唐怡, 刘良云, 黄文江, 等. 土壤背景对冠层NDVI的影响分析[J]. 遥感技术与应用, 2006, 21( 2): 142- 148.
	TANG Y, LIU L Y, HUANG W J, et al. Analysis on the influence of soil backgrounds on canopy NDVI[J]. Remote sensing technology and application, 2006, 21( 2): 142- 148.
51	王鑫, 冯建东, 王锐婷, 等. 基于SPOT-NDVI的川西平原水稻生育期监测分析[J]. 中国农学通报, 2013, 29( 36): 39- 46.
	WANG X, FENG J D, WANG R T, et al. The monitoring analysis of paddy rice growing periods in the western Sichuan Plain based on SPOT-NDVI[J]. Chinese agricultural science bulletin, 2013, 29( 36): 39- 46.
52	白燕英, 高聚林, 张宝林. 基于NDVI与EVI的作物长势监测研究[J]. 农业机械学报, 2019, 50( 9): 153- 161.
	BAI Y Y, GAO J L, ZHANG B L. Monitoring of crops growth based on NDVI and EVI[J]. Transactions of the Chinese society for agricultural machinery, 2019, 50( 9): 153- 161.
53	唐延林. 水稻高光谱特征及其生物理化参数模拟与估测模型研究[D]. 杭州: 浙江大学, 2004.
	TANG Y L. Study on the hyperspectral characteristics and simulating and estimating models about biophysical and biochemical parameters of rice[D]. Hangzhou: Zhejiang University, 2004.
54	FU T Y, TIAN S F, ZHAN Q. Phenological analysis and yield estimation of rice based on multi-spectral and SAR data in Maha Sarakham, Thailand[J]. Journal of spatial science, 2024, 69( 1): 149- 165.
55	CURRAN P J, STEVEN M D. Multispectral remote sensing for the estimation of green leaf area index[J]. Philosophical transactions of the royal society of London series A, mathematical and physical sciences, 1983, 309( 1508): 257- 270.
56	SHENG R T, HUANG Y H, CHAN P C, et al. Rice growth stage classification via RF-based machine learning and image processing[J]. Agriculture, 2022, 12( 12): ID 2137.
57	UPPALA D, SOMEPALLI V, VENKATA R K, et al. Identification of optimal single date for rice crop discrimination and relationships between backscatter and biophysical parameters using RISAT-1 hybrid polarimetric SAR data[J]. Geocarto international, 2021, 36( 17): 2010- 2022.
58	CAMPOS-TABERNER M, GARCÍA-HARO F J, BUSETTO L, et al. A critical comparison of remote sensing leaf area index estimates over rice-cultivated areas: From sentinel-2 and landsat-7/8 to MODIS, GEOV1 and EUMETSAT polar system[J]. Remote sensing, 2018, 10( 5): ID 763.
59	SAKAMOTO T, SHIBAYAMA M, KIMURA A, et al. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth[J]. ISPRS journal of photogrammetry and remote sensing, 2011, 66( 6): 872- 882.
60	AN Q, GAO W L, YU L N, et al. Research on crop identification method based on two-phase classification using remote sensing in large scale[J]. 2010 world automation congress, WAC 2010, 2010: 7- 11.
61	DESAI S V, BALASUBRAMANIAN V N, FUKATSU T, et al. Automatic estimation of heading date of paddy rice using deep learning[J]. Plant methods, 2019, 15: ID 76.
62	徐建鹏, 王杰, 徐祥, 等. 基于RAdam卷积神经网络的水稻生育期图像识别[J]. 农业工程学报, 2021, 37( 8): 143- 150.
	XU J P, WANG J, XU X, et al. Image recognition for different developmental stages of rice by RAdam deep convolutional neural networks[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37( 8): 143- 150.
63	HUANG Z F, GONG L, LIU C L, et al. Measurement of rice tillers based on magnetic resonance imaging[J]. IFAC-PapersOnLine, 2016, 49( 16): 254- 258.
64	YANG W N, GUO Z L, HUANG C L, et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice[J]. Nature communications, 2014, 5: ID 5087.
65	李琼砚, 高云鹏, 翁雨辰. 一种基于RGB图像的小麦分蘖数自动检测方法: CN107993243B[P]. 2020-06-23.
	LI Q Y, GAO Y P, WENG Y C. Method for automatically detecting wheat tiller number based on RGB images: CN107993243B[P]. 2020-06-23.
66	曹中盛, 李艳大, 叶春, 等. 基于高光谱的双季稻分蘖数监测模型[J]. 农业工程学报, 2020, 36( 4): 185- 192.
	CAO Z S, LI Y D, YE C, et al. Model for monitoring tiller number of double cropping rice based on hyperspectral reflectance[J]. Transactions of the Chinese society of agricultural engineering, 2020, 36( 4): 185- 192.
67	FANG Y, QIU X L, GUO T, et al. An automatic method for counting wheat tiller number in the field with terrestrial LiDAR[J]. Plant methods, 2020, 16: ID 132.
68	王敏娟, 刘小丫, 马啸霄, 等. 基于堆叠沙漏网络的单分蘖水稻植株骨架提取[J]. 农业工程学报, 2021, 37( 24): 149- 157.
	WANG M J, LIU X Y, MA X X, et al. Skeleton extraction method of single tillers rice based on stacked hourglass network[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37( 24): 149- 157.
69	LUO J Q, DAI B S, CHANG P H, et al. Determination of rice leaf midrib deflection in field environment by using semantic segmentation and shortest distance algorithm[J]. Computers and electronics in agriculture, 2023, 215: ID 108326.
70	RASTI S, BLEAKLEY C J, HOLDEN N M, et al. A survey of high resolution image processing techniques for cereal crop growth monitoring[J]. Information processing in agriculture, 2022, 9( 2): 300- 315.
71	蔡竹轩, 蔡雨霖, 曾凡国, 等. 基于改进YOLOv5l的田间水稻稻穗识别[J]. 华南农业大学学报, 2024, 45( 1): 108- 115.
	CAI Z X, CAI Y L, ZENG F G, et al. Rice panicle recognition in field based on improved YOLOv5l model[J]. Journal of South China agricultural university, 2024, 45( 1): 108- 115.
72	段凌凤, 熊雄, 刘谦, 等. 基于深度全卷积神经网络的大田稻穗分割[J]. 农业工程学报, 2018, 34( 12): 202- 209.
	DUAN L F, XIONG X, LIU Q, et al. Field rice panicle segmentation based on deep full convolutional neural network[J]. Transactions of the Chinese society of agricultural engineering, 2018, 34( 12): 202- 209.
73	刘哲, 袁冬根, 王恩. 基于改进Bayes抠图算法的麦穗小穗自动计数方法[J]. 中国农业科技导报, 2020, 22( 8): 75- 82.
	LIU Z, YUAN D G, WANG E. Automatic counting method of wheat grain based on improved Bayes matting algorithm[J]. Journal of agricultural science and technology, 2020, 22( 8): 75- 82.
74	DANDRIFOSSE S, ENNADIFI E, CARLIER A, et al. Deep learning for wheat ear segmentation and ear density measurement: From heading to maturity[J]. Computers and electronics in agriculture, 2022, 199: ID 107161.
75	支俊俊, 董娅, 鲁李灿, 等. 基于无人机RGB影像的玉米种植信息高精度提取方法[J]. 农业工程学报, 2021, 37( 18): 48- 54.
	ZHI J J, DONG Y, LU L C, et al. High-precision extraction method for maize planting information based on UAV RGB images[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37( 18): 48- 54.
76	ZHOU Q Y, GUO W, CHEN N, et al. Analyzing nitrogen effects on rice panicle development by panicle detection and time-series tracking[J]. Plant phenomics, 2023, 5: ID 48.
77	LAI J K, LIN W S. Real-time detection of rice growth phase transition for panicle nitrogen application timing assessment[J]. Agronomy, 2021, 11( 12): ID 2465.
78	XIONG X, DUAN L F, LIU L B, et al. Panicle-SEG: A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization[J]. Plant methods, 2017, 13: ID 104.
79	GUO Z Y, YANG C H, YANG W N, et al. Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field[J]. Journal of experimental botany, 2022, 73( 19): 6575- 6588.
80	TAN S Y, LU H H, YU J, et al. In-field rice panicles detection and growth stages recognition based on RiceRes2Net[J]. Computers and electronics in agriculture, 2023, 206: ID 107704.
81	马良勇, 李西明, 朱旭东. 水稻株高性状的研究进展[J]. 福建稻麦科技, 2001, 19( 4): 20- 23.
	MA L Y, LI X M, ZHU X D. Research progress on plant height traits of rice[J]. Fujian science and technology of rice and wheat, 2001, 19( 4): 20- 23.
82	杨进, 明博, 杨飞, 等. 利用无人机影像监测不同生育阶段玉米群体株高的精度差异分析[J]. 智慧农业(中英文), 2021, 3( 3): 129- 138.
	YANG J, MING B, YANG F, et al. The accuracy differences of using unmanned aerial vehicle images monitoring maize plant height at different growth stages[J]. Smart agriculture, 2021, 3( 3): 129- 138.
83	GUO T, FANG Y, CHENG T, et al. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages[J]. Computers and electronics in agriculture, 2019, 165: ID 104959.
84	SHAO Y, FAN X T, LIU H, et al. Rice monitoring and production estimation using multitemporal RADARSAT[J]. Remote sensing of environment, 2001, 76( 3): 310- 325.
85	RAMYA S, KALIMUTHU K. Improved hybrid CNN bidirectional long short-term memory with BIRCH based segmentation for crop and weed classification[C]// 2024 2nd International Conference on Networking and Communications (ICNWC). Piscataway, New Jersey, USA: IEEE, 2024: 1- 7.
86	ZHAO L C, GUO W, WANG J, et al. An efficient method for estimating wheat heading dates using UAV images[J]. Remote sensing, 2021, 13( 16): ID 3067.
87	HONG W Y, LI Z Q, FENG X Q, et al. Estimating key phenological dates of multiple rice accessions using unmanned aerial vehicle-based plant height dynamics for breeding[J]. Rice science, 2024, 31( 5): 617- 628.
88	DEVIA C A, ROJAS J P, PETRO E, et al. High-throughput biomass estimation in rice crops using UAV multispectral imagery[J]. Journal of intelligent & robotic systems, 2019, 96( 3): 573- 589.
89	CHOUDHARY K, UNIVERSITY S N R, et al. Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems[J]. Computer optics, 2021, 45( 3): 778- 789.
90	GUO Y H, XIAO Y, LI M W, et al. Identifying crop phenology using maize height constructed from multi-sources images[J]. International journal of applied earth observation and geoinformation, 2022, 115: ID 103121.
91	HE Z, LI S H, WANG Y, et al. Monitoring rice phenology based on backscattering characteristics of multi-temporal RADARSAT-2 datasets[J]. Remote sensing, 2018, 10( 2): ID 340.
92	MOEINI RAD A, ASHOURLOO D, SALEHI SHAHRABI H, et al. Developing an automatic phenology-based algorithm for rice detection using sentinel-2 time-series data[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2019, 12( 5): 1471- 1481.
93	COLORADO J D, CALDERON F, MENDEZ D, et al. A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops[J]. PLoS One, 2020, 15( 10): ID e0239591.
94	DONG J W, XIAO X M, MENARGUEZ M A, et al. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine[J]. Remote sensing of environment, 2016, 185: 142- 154.
95	CHOUDHARY K, SHI W, DONG Y, et al. Random Forest for rice yield mapping and prediction using Sentinel-2 data with Google Earth Engine[J]. Advances in space research, 2022, 70( 8): 2443- 2457.
96	ZHANG J H, LIN X M, JIANG C Y, et al. Predicting rice phenology across China by integrating crop phenology model and machine learning[J]. Science of the total environment, 2024, 951: ID 175585.
97	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39( 6): 1137- 1149.
98	王栋, 陈佳玮, 沈利言, 等. 基于图像的水稻穗粒相关性状智能检测算法研究[J]. 植物生理学报, 2022, 58( 5): 957- 971.
	WANG D, CHEN J W, SHEN L Y, et al. The development of a vision-based phenotypic analysis algorithm for measuring spikelet-related traits in rice[J]. Plant physiology journal, 2022, 58( 5): 957- 971.
99	RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[M]// Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham: Springer International Publishing, 2015: 234- 241.
100	CONG N, WANG T, NAN H J, et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: A multimethod analysis[J]. Global change biology, 2013, 19( 3): 881- 891.
101	ZHOU M, ZHENG H B, HE C, et al. Wheat phenology detection with the methodology of classification based on the time-series UAV images[J]. Field crops research, 2023, 292: ID 108798.
102	杜颖, 蔡义承, 谭昌伟, 等. 基于超像素分割的田间小麦穗数统计方法[J]. 中国农业科学, 2019, 52( 1): 21- 33.
	DU Y, CAI Y C, TAN C W, et al. Field wheat ears counting based on superpixel segmentation method[J]. Scientia agricultura sinica, 2019, 52( 1): 21- 33.
103	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40( 4): 834- 848.
104	MAHADEVAN K, PUNITHA A, SURESH J. Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network[J]. E-prime - advances in electrical engineering, electronics and energy, 2024, 8: ID 100534.
105	LIAO J, WANG Y, YIN J N, et al. Segmentation of rice seedlings using the YCrCb color space and an improved otsu method[J]. Agronomy, 2018, 8( 11): ID 269.
106	DUONG H T, HOANG V T. Dimensionality reduction based on feature selection for rice varieties recognition[C]// 2019 4th International Conference on Information Technology (InCIT). Piscataway, New Jersey, USA: IEEE, 2019: 199- 202.
107	MENDES J J A, FREITAS M L B, SIQUEIRA H V, et al. Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach[J]. Biomedical signal processing and control, 2020, 59: ID 101920.
108	JAIN S, SALAU A O, MENG W. An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins[J]. Cogent engineering, 2019, 6( 1): ID 1599537.
109	王尧, 卓莉, 易沵泺, 等. 湖南省单、双季稻识别与生育期提取研究[J]. 地理科学进展, 2015, 34( 10): 1306- 1315.
	WANG Y, ZHUO L, YI M L, et al. Identification of single/double-season paddy rice and retrieval of growth periods in Hunan Province[J]. Progress in geography, 2015, 34( 10): 1306- 1315.
110	SAKAMOTO T, YOKOZAWA M, TORITANI H, et al. A crop phenology detection method using time-series MODIS data[J]. Remote sensing of environment, 2005, 96( 3/4): 366- 374.
111	MUHARAM F M, NURULHUDA K, ZULKAFLI Z, et al. UAV- and random-forest-AdaBoost (RFA)-based estimation of rice plant traits[J]. Agronomy, 2021, 11( 5): ID 915.
112	QIN J L, HU T C, YUAN J H, et al. Deep-learning-based rice phenological stage recognition[J]. Remote sensing, 2023, 15( 11): ID 2891.
113	SAKEEF N, SCANDOLA S, KENNEDY C, et al. Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data[J]. Computational and structural biotechnology journal, 2023, 21: 3183- 3195.
114	YANG Z Y, YU Z R, WANG X Y, et al. Estimation of millet aboveground biomass utilizing multi-source UAV image feature fusion[J]. Agronomy, 2024, 14( 4): ID 701.
115	郑紫瑞, 赵辉杰, 位盼盼, 等. 集成多源遥感数据与生育期时序光谱特征的水稻种植面积提取[J]. 河南农业科学, 2023, 52( 10): 153- 161.
	ZHENG Z R, ZHAO H J, WEI P P, et al. Integration of multi-source remote sensing data and temporal spectral features of growth stages for rice planting area extraction[J]. Journal of Henan agricultural sciences, 2023, 52( 10): 153- 161.
116	BARTZ-BEIELSTEIN T, REHBACH F, SEN A, et al. Surrogate model based hyperparameter tuning for deep learning with SPOT[EB/OL]. arXiv: 2105. 14625v 2, 2021.
117	AJAYI O G, IBRAHIM P O, ADEGBOYEGA O S. Effect of hyperparameter tuning on the performance of YOLOv8 for multi crop classification on UAV images[J]. Applied sciences, 2024, 14( 13): ID 5708.
118	KUMAR U, LAZA M R, SOULIÉ J C, et al. Compensatory phenotypic plasticity in irrigated rice: Sequential formation of yield components and simulation with SAMARA model[J]. Field crops research, 2016, 193: 164- 177.

设备类型	设备名称	传感器类型	空间分辨率	光谱范围
高空遥感	Sentinel-2 ^{［ 16］}	多光谱传感器	10 m	可见光至短波红外
	HJ-1A/1B ^{［ 17］}	CCD传感器	30 m	可见光至近红外
	Landsat 8 ^{［ 12］}	OLI传感器	30 m	可见光至短波红外
	MODIS ^{［ 14］}	成像光谱仪	250~1 000 m	可见光至短波红外
低空遥感	Parrot Bluegrass ^{［ 12］}	多光谱传感器	亚米级	可见光至近红外
	Phantom 4 Pro ^{［ 15］}	RGB传感器	亚米级	可见光
	PrecisionHawk Lancaster ^{［ 15］}	多光谱和热成像传感器	亚米级	近红外波段
	Trimble UX5 ^{［ 15］}	高光谱传感器	亚米级	近红外光谱数据
近地设备	ASD FieldSpec Pro ^{［ 10］}	高光谱传感器	点测量	350~2 500 nm
	GreenSeeker ^{［ 10］}	多光谱传感器	点测量	可见光至近红外
	CROPSCAN ^{［ 13］}	多光谱传感器	点测量	可见光至近红外

群体量化指标	可识别生育期以及预测效果（1~5级）
比值植被指数（Ratio Vegetation Index， RVI） ^{［ 49］}	拔节期（4）、孕穗期（3）、抽穗期（2）
NDVI ^{［ 50， 51］}	分蘖期（3）、拔节期（3）、孕穗期（2）、抽穗扬花期（Heading and flowering stage）（3）、开花期（Flowering Stage）（1）、灌浆期（2）
EVI ^{［ 52］}	裸土（3）、淹水（4）、营养生长（3）、拔节期（4）、生殖生长（Reproductive Growth）（4）、孕穗期（3）、抽穗期（3）、成熟期（5）
SAVI ^{［ 49， 53］}	拔节期（5）、孕穗期（3）、抽穗期（3）
MSAVI ^{［ 53］}	拔节期（4）、孕穗期（4）、抽穗期（4）
绿色植被指数（GVI - Green Vegetation Index） ^{［ 49］}	拔节期（4）、孕穗期（3）、抽穗期（2）
光谱反射率（Spectral Reflectance） ^{［ 44］}	分蘖期（5）、拔节孕穗期（5）、抽穗扬花期（3）、灌浆成熟期（5）
TI ^{［ 44］}	苗期（3）、分蘖期（3）、抽穗期（4）、灌浆期（4）、成熟期（3）
SAR ^{［ 46， 54］}	早期移栽期（Early Transplanting Stage）（2）、分蘖期（3）、孕穗期（4）、抽穗期（4）、成熟期（3）
LAI ^{［ 55］}	分蘖期（3）、拔节期（2）、孕穗期（4）、抽穗期（2）、齐穗期（Full Heading Stage）（3）、乳熟期（2）
AGB ^{［ 35］}	分蘖期-拔节期（2）、分蘖期-孕穗期（3）、拔节期-孕穗期（2）

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