Monitoring of Direct‑seeded Rice SPAD values Based on UAV Remote Sensing Technology

doi:10.15933/j.cnki.1004-3268.2025.09.016

Abstract

Abstract: Accurately obtaining chlorophyll content in direct‑seeded rice is of great significance for innovating unmanned rice direct seedling cultivation management technologies.Field experiments were conducted to identify the optimal monitoring model for the relative chlorophyll content（SPAD value）in leaves of direct‑seeded rice.This study systematically examined the correlations between 13 commonly used multispectral feature indices and SPAD values，followed by a comparative analysis of SPAD estimation results derived from four modeling approaches：Particle swarm optimization‑support vector machine（PSO‑SVM），random forest（RF），radial basis function（RBF）neural network，and least squares support vector machine（LSSVM）.The results showed that the SPAD value of direct‑seeded rice leaves exhibited significant variation with growth progression under different treatments，and the SPAD values at the same growth stage generally followed the trend：N4（N 240 kg/ha）>N3（N 195 kg/ha）>N2（N 150 kg/ha）>N1（N 75 kg/ha）>N0（N 0 kg/ha）.During the three critical growth periods（the tillering，jointing，heading periods）of direct‑seeded rice，the vegetation indices NDVI，RVI，SAVI，CI_green，and GNDVI all showed strong correlations with SPAD values，with the absolute values of correlation coefficients reaching 0.838，0.783，0.838，0.671，and 0.690，respectively. Independent validation using PSO‑SVM，RF，RBF，and LSSVM models yielded determination coefficients（R_cv²）of 0.770，0.771，0.857，and 0.773，respectively.This indicates that the RBF neural network‑based model provides the best predictive performance for monitoring SPAD values in the leaves of direct‑seeded rice.

Key words: Rice, SPAD values, Direct?seeding, Unmanned aerial vehicle, Radial basis function neural network

摘要： 准确获取直播水稻叶绿素含量信息对创新水稻无人化直播栽培管理技术具有重要意义。为筛选直播水稻叶片叶绿素相对含量（SPAD值）的最优监测模型，于2022—2023年连续2 a设置了大田直播水稻试验，分析了13个常用多光谱特征指数与SPAD值的相关关系，并对基于粒子群算法优化支持向量机（Particle swarm optimization‑support vector machine，PSO-SVM）、随机森林（Random forest，RF）、径向基函数（Radial basis function，RBF）神经网络和最小二乘支持向量机（Least squares support vector machine，LSSVM）模型的SPAD值估算结果进行了对比分析。结果表明，不同氮肥处理下直播水稻叶片SPAD值因生育时期时间节点不同而存在差异，且不同的肥力下直播水稻叶片SPAD值高低总体表现为N4处理（N 240 kg/hm^²）>N3 处理（N 195 kg/hm^²）>N2 处理（N 150 kg/hm^²）>N1 处理（N 75 kg/hm^²）>N0 处理（N 0 kg/hm^²）；在水稻分蘖期、拔节期、抽穗期3个生育时期，NDVI、RVI、SAVI、CI_green、GNDVI植被指数与SPAD值的相关性均相对较好，相关系数绝对值分别达到0.838、0.783、0.838、0.671、0.690。基于PSOSVM、RF、RBF、LSSVM模型的独立验证Rcv2分别为0.770、0.771、0.857、0.773，可见RBF模型可更好地用于监测直播水稻叶片SPAD值。

关键词: 水稻, SPAD值, 直播, 无人机, 径向基神经网络

CLC Number:

S127

JIANG Xun, LIU Wei, ZHANG Dahong, YOU Hao, FU Bin, LI Yanli, LU Bilin. Monitoring of Direct‑seeded Rice SPAD values Based on UAV Remote Sensing Technology[J]. Journal of Henan Agricultural Sciences, 2025, 54(9): 149-158.

江勋, 刘伟, 张大弘, 游昊, 付斌, 李燕丽, 卢碧林. 基于无人机遥感技术的直播水稻叶片SPAD 值监测[J]. 河南农业科学, 2025, 54(9): 149-158.

Figures/Tables 5

References

［1］王跃星，魏祥进，徐春春，等. 我国水稻种业发展现状与对策浅析［J］.中国稻米，2022，28（5）：62‑65.
WANG Y X，WEI X J，XU C C，et al. Current situation and countermeasures of rice breeding and seed industry development in China［J］. China Rice，2022，28（5）：62‑65.

［2］KHUSH K K，WATERS D. Lessons from the PROVE‑IT trial.Higher dose of potent statin better for high‑risk patients［J］. Cleveland Clinic Journal of Medicine，2004，71（8）：609‑616.

［3］蒋敏，李秀彬，辛良杰，等. 南方水稻复种指数变化对国家粮食产能的影响及其政策启示［J］.地理学报，2019，74（1）：32‑43.

JIANG M，LI X B，XIN L J，et al. The impact of paddy rice multiple cropping index changes in Southern China on national grain production capacity and its policy implications［J］.Acta Geographica Sinica，2019，74（1）：32‑43.

［4］翁文安，邢志鹏，胡群，等. 无人化旱直播模式对水稻产量、品质和经济效益的影响［J］.中国农业科学，2024，57（17）：3350‑3365.

WENG W A，XING Z P，HU Q，et al. Effects of unmanned dry direct‑seeded mode on yield，grain quality of rice and its economic benefits［J］. Scientia Agricultura Sinica，2024，57（17）：3350‑3365.

［5］何伟灼，刘威，姜锐，等. 无人机点射式水稻播种装置控制系统设计与试验［J］.农业工程学报，2022，38（18）：51‑61.

HE W Z，LIU W，JIANG R，et al.Control system design and experiments of UAV shot seeding device for rice［J］.Transactions of the Chinese Society of Agricultural Engineering，2022，38（18）：51‑61.

［6］朱海滨，胡群，陆喜瞻，等.无人飞播水稻生育特征与丰产关键技术研究进展［J］.农业工程学报，2024，40（11）：1‑13.

ZHU H B，HU Q，LU X Z，et al. Research progress in growth characteristics and key techniques for the high yield of unmanned aerial seeding rice［J］.Transactions of the Chinese Society of Agricultural Engineering，2024，40（11）：1‑13.

［7］张晓丽，陶伟，高国庆，等. 直播栽培对双季早稻生育期、抗倒伏能力及产量效益的影响［J］.中国农业科学，2023，56（2）：249‑263.

ZHANG X L，TAO W，GAO G Q，et al. Effects of direct seeding cultivation method on growth stage，lodging resistance and yield benefit of double‑cropping early rice［J］. Scientia Agricultura Sinica，2023，56（2）：249‑263.

［8］廖萍，翁文安，高辉，等.我国直播稻栽培技术应用现状与发展建议［J］.中国农业科学，2024，57（24）：4854‑4870.

LIAO P，WENG W A，GAO H，et al.Application status and development suggestion of direct‑seeding rice cultivation in China［J］. Scientia Agricultura Sinica，2024，57（24）：4854‑4870.

［9］唐志强，张丽颖，何娜，等. 机械旱直播对水稻生育进程、光合特性及产量的影响［J］.作物杂志，2021（5）：87‑94.

TANG Z Q，ZHANG L Y，HE N，et al.Effects of mechanical direct dry seeding on rice growth，photosynthetic characteristics and yield［J］. Crops，2021（5）：87‑94.

［10］裴信彪，吴和龙，马萍，等. 基于无人机遥感的不同施氮水稻光谱与植被指数分析［J］.中国光学，2018，11（5）：832‑840.

PEI X B，WU H L，MA P，et al.Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing［J］. Chinese Optics，2018，11（5）：832‑840.

［11］VILLANO L，RAVIZ J，PAGUIRIGAN N M，et al.Separability of transplanted and direct seeded rice using multi‑temporal Sentinel‑1A data［J］.The International Archives of the Photogrammetry，Remote Sensing and Spatial Information Sciences，2019，XLII‑4/W19：471‑478.

［12］SIMKIN A J，KAPOOR L，DOSS C G P，et al.The role of photosynthesis related pigments in light harvesting，photoprotection and enhancement of photosynthetic yield in planta［J］. Photosynthesis Research，2022，152（1）：23‑42.

［13］王楠，陈春玲，相爽，等. 基于叶片双层辐射传输机理的水稻叶绿素含量反演［J］.农业工程学报，2024，40（17）：171‑178.

WANG N，CHEN C L，XIANG S，et al.Inversing chlorophyll contents in rice using radiation transport of leaf bilayer［J］.Transactions of the Chinese Society of Agricultural Engineering，2024，40（17）：171‑178.

［14］周敏姑，邵国敏，张立元，等.无人机多光谱遥感反演冬小麦SPAD值［J］.农业工程学报，2020，36（20）：125‑133.

ZHOU M G，SHAO G M，ZHANG L Y，et al. Inversion of SPAD value of winter wheat by multispectral remote sensing of unmanned aerial vehicles［J］.Transactions of the Chinese Society of Agricultural Engineering，2020，36（20）：125‑133.

［15］TIAN F K，ZHOU J F，RANSOM C J，et al.Estimating corn leaf chlorophyll content using airborne multispectral imagery and machine learning［J］.Smart Agricultural Technology，2025，10：100719.

［16］刘一博，裴杰，方华军，等.利用无人机影像反演水稻SPAD值的最优空间窗口确定［J］.农业工程学报，2023，39（19）：165‑174.

LIU Y B，PEI J，FANG H J，et al.Optimizing spatial window selection for rice SPAD value retrieval using multispectral UAV images［J］.Transactions of the Chinese Society of Agricultural Engineering，2023，39（19）：165‑174.

［17］田婷，张青，徐雯. 基于无人机多光谱影像的水稻冠层SPAD值预测研究［J］.中国农学通报，2023，39（4）：149‑153.

TIAN T，ZHANG Q，XU W. Prediction of rice canopy SPAD value based on UAV multispectral images［J］.Chinese Agricultural Science Bulletin，2023，39（4）：149‑153.

［18］GU Q，HUANG F D，LOU W D，et al.Unmanned aerial vehicle‑based assessment of rice leaf chlorophyll content dynamics across genotypes［J］.Computers and Electronics in Agriculture，2024，221：108939.

［19］WANG Y W，TAN S Y，JIA X N，et al.Estimating relative chlorophyll content in rice leaves using unmanned aerial vehicle multi‑spectral images and spectral‑textural analysis［J］. Agronomy，2023，13（6）：1541.

［20］长江大学.一种无人机播种管道堵塞报警系统：CN202321434971. 8［P］.2024‑03‑19.

Yangtze University. A drone seeding pipeline blockage alarm system：CN202321434971.8［P］.2024‑03‑19. ［21］高小梅，李燕丽，卢碧林，等. 基于高光谱和数字图像特征指数的受渍冬小麦SPAD估算［J］.应用生态学报，2021，32（3）：959‑966.

GAO X M，LI Y L，LU B L，et al. Estimation of SPAD value in waterlogged winter wheat based on characteristic indices of hyperspectral and digital image［J］. Chinese Journal of Applied Ecology，2021，32（3）：959‑966.

［22］魏鹏飞，徐新刚，李中元，等. 基于无人机多光谱影像的夏玉米叶片氮含量遥感估测［J］.农业工程学报，2019，35（8）：126‑133.

WEI P F，XU X G，LI Z Y，et al.Remote sensing estimation of nitrogen content in summer maize leaves based on multispectral images of UAV[J］.Transactions of the Chinese Society of Agricultural Engineering，2019，35（8）：126‑133.

［23］李志博，李亚芹，赵浣旻，等. 基于GNDVI指数的土壤- 水稻冠层变量施氮决策方案研究［J］.中国农机化学报，2022，43（4）：160‑165.

LI Z B，LI Y Q，ZHAO H M，et al. Study on the decision‑making scheme of soil‑rice canopy variable nitrogen application based on GNDVI index［J］.Journal of Chinese Agricultural Mechanization，2022，43（4）：160‑165.

［24］ZHAO L，QING S H，WANG F，et al. Prediction of rice yield based on multi‑source data and hybrid LSSVM algorithms in China［J］.International Journal of Plant Production，2023，17（4）：693‑713.

［25］张悦，陈冰，李旭文，等. 基于Sentinel-2与随机森林算法的太湖水生植被分布监测［J］.环境监控与预警，2023，15（6）：42‑49.

ZHANG Y，CHEN B，LI X W，et al. Monitoring aquatic vegetation distribution of Taihu Lake from Sentinel‑2 and random forest algorithm［J］.Environmental Monitoring and Forewarning，2023，15（6）：42‑49.

［26］XU W C，YANG W G，CHEN S D，et al.Establishing a model to predict the single boll weight of cotton in northern Xinjiang by using high resolution UAV remote sensing data［J］.Computers and Electronics in Agriculture，2020，179：105762.

［27］RASTGOU M，BAYAT H，MANSOORIZADEH M，et al.Estimating soil water retention curve by extreme learning machine，radial basis function，M5 tree and modified group method of data handling approaches［J］.Water Resources Research，2022，58（4）：e2021WR031059.

［28］LI Y L，GAO X M，LI T，et al.Estimation of the net photosynthetic rate for waterlogged winter wheat based on digital image technology［J］.Agronomy Journal，2023，115（1）：230‑241.

［29］LI W C，CHEN R，MA D J，et al. Tracking autumn photosynthetic phenology on Tibetan Plateau grassland with the green‑red vegetation index［J］.Agricultural and Forest Meteorology，2023，339：109573.

［30］LI M，CHU R H，SHA X Z，et al.Hyperspectral characteristics and scale effects of leaf and canopy of summer maize under continuous water stresses［J］.Agriculture，2021，11（12）：1180.

［31］TŮMA L，KUMHÁLOVÁ J，KUMHÁLA F，et al.The noise‑reduction potential of Radar Vegetation Index for crop management in the Czech Republic［J］.Precision Agriculture，2022，23（2）：450‑469.

［32］XIE Z Y，ZHU W Q，HE B K，et al.A background‑free phenology index for improved monitoring of vegetation phenology［J］.Agricultural and Forest Meteorology，2022，315：108826.

［33］NAGY A，SZABÓ A，ADENIYI O D，et al.Wheat yield forecasting for the Tisza River Catchment using landsat 8 NDVI and SAVI time series and reported crop statistics［J］.Agronomy，2021，11（4）：652.

［34］BURNS B W，GREEN V S，HASHEM A A，et al.Determining nitrogen deficiencies for maize using various remote sensing indices［J］.Precision Agriculture，2022，23（3）：791‑811.

［35］ARSHAD S，KAZMI J H，JAVED M G，et al.Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan，South Asia［J］.European Journal of Agronomy，2023，147：126837.

［36］吕宙，易秉怀，陈平平，等. 施氮量与移栽密度对小粒型杂交水稻产量形成的影响［J］.中国水稻科学，2024，38（4）：422‑436.

LÜ Z，YI B H，CHEN P P，et al. Effects of nitrogen application rate and transplanting density on yield formation of small seed hybrid rice［J］.Chinese Journal of Rice Science，2024，38（4）：422‑436.

［37］童纪氚，戎雪利，任萍，等. 水稻无人机直播产量、效益分析及技术要点［J］.中国稻米，2024，30（1）：98‑100.

TONG J C，RONG X L，REN P，et al. Yield and benefit analysis of rice drone direct seeding and its technical points［J］.China Rice，2024，30（1）：98‑100.

［38］罗友谊，王慰亲，郑华斌，等. 不同机械有序种植方式对水稻生长特性及产量的影响［J］. 中国农业科技导报，2021，23（7）：162‑171.

LUO Y Y，WANG W Q，ZHENG H B，et al.Influences of different mechanical and orderly planting methods on growth characteristics and yield of rice［J］. Journal of Agricultural Science and Technology，2021，23（7）：162‑171.

［39］刘红江，郑建初，陈留根，等. 不同播栽方式对水稻生长发育特性的影响［J］.生态学杂志，2013，32（9）：2326‑2331.

LIU H J，ZHENG J C，CHEN L G，et al. Effects of different planting modes on the growth and development characteristics of rice［J］. Chinese Journal of Ecology，2013，32（9）：2326‑2331.

［40］肖大康，丁紫娟，胡仁，等.我国长江流域不同水稻种植区域氮肥和栽培管理策略研究［J］.植物营养与肥料学报，2023，29（11）：2018‑2029.

XIAO D K，DING Z J，HU R，et al.Study of nitrogen fertilizer management and cultivation strategies in different rice planting areas of the Yangtze River Basin of China［J］.Journal of Plant Nutrition and Fertilizers，2023，29（11）：2018‑2029.

［41］YANG H，YANG J P，LV Y M，et al. SPAD values and nitrogen nutrition index for the evaluation of rice nitrogen status［J］. Plant Production Science，2014，17 （1）：81‑92.

［42］KHOSE S B， MAILAPALLI D R. UAV‑based multispectral image analytics and machine learning for predicting crop nitrogen in rice ［J］. Geocarto International，2024，39（1）：2373867.

［43］YANG X H，WANG F M，HUANG J F，et al.Comparison between radial basis function neural network and regression model for estimation of rice biophysical parameters using remote sensing［J］.Pedosphere，2009，19（2）：176‑188.

［44］SHAO Y N，ZHAO C J，BAO Y D，et al. Quantification of nitrogen status in rice by least squares support vector machines and reflectance spectroscopy［J］. Food and Bioprocess Technology，2012，5（1）：100‑107.

［45］冯浩，杨祯婷，陈浩，等. 基于无人机多光谱影像的夏玉米SPAD估算模型研究［J］.农业机械学报，2022，53（10）：211‑219.
FENG H，YANG Z T，CHEN H，et al. Estimation of summer maize SPAD based on UAV multispectral images［J］. Transactions of the Chinese Society for Agricultural Machinery，2022，53（10）：211‑219.

［46］WANG W，SUN N，BAI B，et al. Prediction of wheat SPAD using integrated multispectral and support vector machines［J］. Frontiers in Plant Science，2024，15：1405068.

［47］鲁向晖，王倩，张海娜，等. 基于无人机多光谱遥感的芳樟矮林SPAD反演［J］.农业机械学报，2023，54（5）：201‑209.
LU X H，WANG Q，ZHANG H N，et al. Inversion of SPAD of Cinnamomum camphora dwarf forest based on UAV multispectral remote sensing［J］.Transactions of the Chinese Society for Agricultural Machinery，2023，54（5）：201‑209.

［48］YU H，XIE T T，PASZCZYNSKI S，et al. Advantages of radial basis function networks for dynamic system design［J］. IEEE Transactions on Industrial Electronics，2011，58（12）：5438‑5450.
［49］张智韬，魏广飞，姚志华，等. 基于无人机多光谱遥感的土壤含盐量反演模型研究［J］.农业机械学报，2019，50（12）：151‑160.
ZHANG Z T，WEI G F，YAO Z H，et al.Soil salt inversion model based on UAV multispectral remote sensing［J］. Transactions of the Chinese Society for Agricultural Machinery，2019，50（12）：151‑160.
［50］CHATTERJEE S，HUANG J Y，HARTEMINK A E.Establishing an empirical model for surface soil moisture retrieval at the U.S. climate reference network using Sentinel‑1 backscatter and ancillary data［J］.Remote Sensing，2020，12（8）：1242.
［51］杨艳华，王才林.不同水稻品种叶片显微结构和超微结构的比较研究［J］.植物研究，2010，30（2）：152‑156.
YANG Y H，WANG C L. Comparative study on the microstructure and ultrastructure for three cultivated varieties of rice［J］. Bulletin of Botanical Research，2010，30（2）：152‑156.
［52］XIONG D L，CHEN J，YU T T，et al. SPAD‑based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics［J］.Scientific Reports，2015，5：13389.

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