浙江农业学报 ›› 2020, Vol. 32 ›› Issue (12): 2232-2243.DOI: 10.3969/j.issn.1004-1524.2020.12.15
收稿日期:
2020-04-17
出版日期:
2020-12-25
发布日期:
2020-12-25
作者简介:
杨红云(1975—),男,江西新干人,硕士,副教授,主要从事农业信息技术研究工作。E-mail: nc_yhy@163.com
基金资助:
YANG Hongyun1(), LUO Jianjun2, SUN Aizhen2, WAN Ying2, YI Wenlong1
Received:
2020-04-17
Online:
2020-12-25
Published:
2020-12-25
摘要:
探究水稻叶片生长外部颜色、几何形态特征与其全含氮量之间的定量描述关系,可以快速且准确地诊断水稻氮素营养状况。研究筛选了全氮含量估测敏感叶位,并比较了基于多元线性回归和机器学习方法的水稻敏感叶位全氮含量估测模型,为构建高性能的氮素营养定量诊断模型提供思路和方法。水稻田间试验于2017—2018年在江西省南昌市成新农场进行,供试水稻品种为两优培九,设置4个施氮水平(施氮水平从低到高为0、210、300和390 kg·hm-2)。在水稻幼穗分化期和齐穗期,分别扫描获取水稻顶部第一完全展开叶叶片(顶1叶)、顶部第二完全展开叶叶片(顶2叶)以及顶部第三完全展开叶叶片(顶3叶)图像,共4 800张图像。通过图像处理技术获取25项水稻扫描叶颜色和几何形态特征,采用多元线性回归进行全氮含量估测,筛选出两个时期的敏感叶位,并应用机器学习方法建立水稻敏感叶位的全氮含量估测模型。与人工测量相比,通过图像处理方法获取的水稻叶片长宽平均相对误差分别为0.328%、3.404%;幼穗分化期顶3叶和齐穗期顶2叶较其他同期叶位更为敏感,且以幼穗分化期顶3叶最为敏感;应用机器学习建立的水稻敏感叶位全氮含量估测模型略优于多元线性回归模型,且采用BP神经网络建模最佳,幼穗分化期顶3叶模型验证集的RMSEv=0.089、MREv=0.034、 $R^{2}_{v}$=0.887,齐穗期顶2叶模型验证集的RMSEv=0.132、MREv=0.046、$R^{2}_{v}$=0.820。幼穗分化期顶3叶和齐穗期顶2叶的叶片图像特征最具有代表性,进行全氮含量估测更具可行性,可作为水稻氮素营养诊断的有效叶位。
中图分类号:
杨红云, 罗建军, 孙爱珍, 万颖, 易文龙. 基于图像特征的水稻叶片全氮含量估测模型研究[J]. 浙江农业学报, 2020, 32(12): 2232-2243.
YANG Hongyun, LUO Jianjun, SUN Aizhen, WAN Ying, YI Wenlong. Study on estimation model of total nitrogen content in rice leaves based on image characteristics[J]. Acta Agriculturae Zhejiangensis, 2020, 32(12): 2232-2243.
图8 水稻图像特征与叶片全氮含量的相关系数分布图 图中特征号1~9代表叶片颜色特征,分别为叶片R、叶片G、叶片B、叶片NRI、叶片NGI、叶片NBI、叶片H、叶片S、叶片I;特征号10~18代表叶鞘颜色特征,分别为叶鞘R、叶鞘G、叶鞘B、叶鞘NRI、叶鞘NGI、叶鞘NBI、叶鞘H、叶鞘S、叶鞘I;特征号19~25代表几何形态特征特征,分别为叶片长度、叶片宽度、叶片周长、叶片面积、面积周长比、面积长度比、长宽比。图中红色值表示在0.010水平双侧显著相关;蓝色值表示在0.050水平双侧显著相关。
Fig.8 Correlation coefficient distribution of rice image characteristics and leaf total nitrogen content Feature numbers 1 to 9 in the figure represented leaf R, leaf G, leaf B, leaf NRI, leaf NGI, leaf NBI, leaf H, leaf S, leaf I, respectively. Feature numbers 10 to 18 represented leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath NRI, leaf sheath NGI, leaf sheath NBI, leaf sheath H, leaf sheath S, leaf sheath I, respectively. Feature numbers 19 to 25 represented leaf length, leaf width, leaf perimeter, leaf area, area perimeter ratio, area length ratio, length width ratio, respectively. The red values showed bilateral significant correlation at 0.010 level; the blue values showed bilateral significant correlation at 0.050 level.
时期及叶位 Period and leaf position | 选择特征 Selected characteristics | 多元线性回归模型 Multivariate regression model | MREv | RMSEv | ||
---|---|---|---|---|---|---|
幼穗分化期顶三叶 3rd leaf of the Young panicle stage | 叶片R、叶片G、叶片NRI、叶片H、叶片宽度 Leaf R, leaf G, leaf NRI, leaf H, leaf width | y=5.800-0.008x1-0.025x2- 4.008x3+0.010x4+0.128x5 | 0.862 | 0.875 | 0.035 | 0.094 |
齐穗期顶二叶 2nd leaf of the full heading stage | 叶片NRI、叶片NBI、叶片H、叶鞘NRI、叶鞘H Leaf NRI, leaf NBI, leaf H, leaf sheath NRI, leaf sheath H | y=-7.917-0.519x1-1.341x2+ 0.118x3+1.804x4-0.002x5 | 0.708 | 0.714 | 0.058 | 0.164 |
齐穗期顶三叶 3rd leaf of the full heading stage | 叶鞘B、叶鞘NGI、叶鞘NBI、叶鞘S、叶鞘I Leaf sheath B, leaf sheath NGI, leaf sheath NBI, Leaf sheath S, leaf sheath I | y=2.743-0.043x1-0.011x2+ 15.004x3-1.175x4-2.380x5 | 0.692 | 0.676 | 0.145 | 0.245 |
表1 多元线性回归建模自变量选择及模型检验结果
Table 1 Multivariate linear regression modeling independent variable selection and model test results
时期及叶位 Period and leaf position | 选择特征 Selected characteristics | 多元线性回归模型 Multivariate regression model | MREv | RMSEv | ||
---|---|---|---|---|---|---|
幼穗分化期顶三叶 3rd leaf of the Young panicle stage | 叶片R、叶片G、叶片NRI、叶片H、叶片宽度 Leaf R, leaf G, leaf NRI, leaf H, leaf width | y=5.800-0.008x1-0.025x2- 4.008x3+0.010x4+0.128x5 | 0.862 | 0.875 | 0.035 | 0.094 |
齐穗期顶二叶 2nd leaf of the full heading stage | 叶片NRI、叶片NBI、叶片H、叶鞘NRI、叶鞘H Leaf NRI, leaf NBI, leaf H, leaf sheath NRI, leaf sheath H | y=-7.917-0.519x1-1.341x2+ 0.118x3+1.804x4-0.002x5 | 0.708 | 0.714 | 0.058 | 0.164 |
齐穗期顶三叶 3rd leaf of the full heading stage | 叶鞘B、叶鞘NGI、叶鞘NBI、叶鞘S、叶鞘I Leaf sheath B, leaf sheath NGI, leaf sheath NBI, Leaf sheath S, leaf sheath I | y=2.743-0.043x1-0.011x2+ 15.004x3-1.175x4-2.380x5 | 0.692 | 0.676 | 0.145 | 0.245 |
模型 Model | 幼穗分化期顶三叶 3rd leaf at the young panicle stage | 齐穗期顶二叶 2nd leaf at the full heading stage | |||||
---|---|---|---|---|---|---|---|
MREv | RMSEv | MREv | RMSEv | ||||
支持向量机Support vector machine | 0.886 | 0.034 | 0.090 | 0.785 | 0.053 | 0.142 | |
BP神经网络BP neural network | 0.887 | 0.034 | 0.089 | 0.820 | 0.046 | 0.132 |
表2 水稻敏感叶位叶片全氮含量估测模型验证结果对比
Table 2 Comparison of validation results of estimation model of total nitrogen content in sensitive leaves of rice
模型 Model | 幼穗分化期顶三叶 3rd leaf at the young panicle stage | 齐穗期顶二叶 2nd leaf at the full heading stage | |||||
---|---|---|---|---|---|---|---|
MREv | RMSEv | MREv | RMSEv | ||||
支持向量机Support vector machine | 0.886 | 0.034 | 0.090 | 0.785 | 0.053 | 0.142 | |
BP神经网络BP neural network | 0.887 | 0.034 | 0.089 | 0.820 | 0.046 | 0.132 |
图9 基于机器学习的幼穗分化期顶3叶模型实际值与预测值拟合结果 a,支持向量机模型;b,BP神经网络模型。图10同。
Fig.9 Fitting results of actual value and predicted value of 3rd leaf at the young panicle stage based on machine learning a, Support vector machine model; b, BP neural network model.Same as Figure 10.
图10 基于机器学习的齐穗期顶2叶模型实际值与预测值拟合结果3 讨论
Fig.10 Fitting results of actual value and predicted value of 2nd leaf at the full heading stage based on machine learning
[1] | YU X J, LU H D, LIU Q Y. Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf[J]. Chemometrics and Intelligent Laboratory Systems, 2018,172:188-193. |
[2] | CAMERON K C, DI H J, MOIR J L. Nitrogen losses from the soil/plant system: a review[J]. Annals of Applied Biology, 2013,162(2):145-173. |
[3] | LEE K J, LEE B W. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis[J]. European Journal of Agronomy, 2013,48:57-65. |
[4] | 李岚涛, 张萌, 任涛, 等. 应用数字图像技术进行水稻氮素营养诊断[J]. 植物营养与肥料学报, 2015,21(1):259-268. |
LI L T, ZHANG M, REN T, et al. Diagnosis of N nutrition of rice using digital image processing technique[J]. Journal of Plant Nutrition and Fertilizer, 2015,21(1):259-268.(in Chinese with English abstract) | |
[5] | YUAN Y, CHEN L, LI M, et al. Diagnosis of nitrogen nutrition of rice based on image processing of visible light[C]//. IEEE, 2016: 228-232. |
[6] | 顾清, 邓劲松, 陆超, 等. 基于光谱和形状特征的水稻扫描叶片氮素营养诊断[J]. 农业机械学报, 2012,43(8):170-174. |
GU Q, DENG J S, LU C, et al. Diagnosis of rice nitrogen nutrition based on spectral and shape characteristics of scanning leaves[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012,43(8):170-174.(in Chinese with English abstract) | |
[7] | 周琼, 杨红云, 杨珺, 等. 基于BP神经网络和概率神经网络的水稻图像氮素营养诊断[J]. 植物营养与肥料学报, 2019,25(1):134-141. |
ZHOU Q, YANG H Y, YANG J, et al. Feasibility study of BP neural network and probabilistic neural network for nitrogen nutrition diagnosis of rice images[J]. Journal of Plant Nutrition and Fertilizers, 2019,25(1):134-141.(in Chinese with English abstract) | |
[8] | 罗正媛, 汤洋. 黑龙江省玉米产量变化的预测分析: 基于支持向量机的实证研究[J]. 农机化研究, 2013,35(2):30-34. |
LUO Z Y, TANG Y. The prediction analysis on maize yield changes in Heilongjiang Province: empirical research based on support vector machine(SVM)[J]. Journal of Agricultural Mechanization Research, 2013,35(2):30-34.(in Chinese with English abstract) | |
[9] | 张建华, 冀荣华, 袁雪, 等. 基于径向基支持向量机的棉花虫害识别[J]. 农业机械学报, 2011,42(8):178-183. |
ZHANG J H, JI R H, YUAN X, et al. Recognition of pest damage for cotton leaf based on RBF-SVM algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011,42(8):178-183.(in Chinese with English abstract) | |
[10] | 朱文静, 毛罕平, 周莹, 等. 基于高光谱图像技术的番茄叶片氮素营养诊断[J]. 江苏大学学报(自然科学版), 2014,35(3):290-294. |
ZHU W J, MAO H P, ZHOU Y, et al. Hyperspectral imaging technology of nitrogen status diagnose for tomato leaves[J]. Journal of Jiangsu University (Natural Science Edition), 2014,35(3):290-294.(in Chinese with English abstract) | |
[11] | 陈利苏. 基于机器视觉技术的水稻氮磷钾营养识别和诊断[D]. 杭州: 浙江大学, 2014. |
CHEN L S. Rice nutrition identification and diagnosis based on machine vision technology[D]. Hangzhou: Zhejiang University, 2014.(in Chinese with English abstract) | |
[12] | 彭莹琼, 廖牧鑫, 张永红, 等. 基于BP神经网络模型的果实蝇自动分类系统[J]. 江西农业大学学报, 2016,38(6):1205-1210. |
PENG Y Q, LIAO M X, ZHANG Y H, et al. A study on the automatic classification system for fruit flies based on BP neural network model[J]. Acta Agriculturae Universitatis Jiangxiensis, 2016,38(6):1205-1210.(in Chinese with English abstract) | |
[13] | 潘圣刚, 黄胜奇, 张帆, 等. 超高产栽培杂交中籼稻的生长发育特性[J]. 作物学报, 2011,37(3):537-544. |
PAN S G, HUANG S Q, ZHANG F, et al. Growth and development characteristics of super-high-yielding mid-season indica hybrid rice[J]. Acta Agronomica Sinica, 2011,37(3):537-544.(in Chinese with English abstract)
DOI URL |
|
[14] | 孙小香, 王芳东, 赵小敏, 等. 基于冠层光谱和BP神经网络的水稻叶片氮素浓度估算模型[J]. 中国农业资源与区划, 2019,40(3):35-44. |
SUN X X, WANG F D, ZHAO X M, et al. The estimation models of rice leaf nitrogen concentration based on canopy spectrum and BP neural network[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2019,40(3):35-44.(in Chinese with English abstract) | |
[15] | 程慧煌, 商庆银, 易振波, 等. 不同产量水平超级杂交稻产量形成特征及其对施肥量的响应[J]. 中国稻米, 2017,23(4):81-88. |
CHENG H H, SHANG Q Y, YI Z B, et al. Effects of fertilizer application rate on yield and population quality of super hybrid rice at different yield levels[J]. China Rice, 2017,23(4):81-88.(in Chinese with English abstract) | |
[16] | 林洪鑫, 潘晓华, 石庆华, 等. 栽插密度与施氮量对双季稻上部三叶叶长和叶角的影响[J]. 作物学报, 2010,36(10):1743-1751. |
LIN H X, PAN X H, SHI Q H, et al. Effects of nitrogen application amount and planting density on angle and length of top three leaves in double-cropping rice[J]. Acta Agronomica Sinica, 2010,36(10):1743-1751.(in Chinese with English abstract) | |
[17] | 孙玉婷, 王映龙, 杨红云, 等. RGB与HSI色彩空间下预测叶绿素相对含量的研究[J]. 浙江农业学报, 2018,30(10):1782-1789. |
SUN Y T, WANG Y L, YANG H Y, et al. Prediction of SPAD in rice leaf based on RGB and HSI color space[J]. Acta Agriculturae Zhejiangensis, 2018,30(10):1782-1789.(in Chinese with English abstract) | |
[18] | 杨红云, 孙爱珍, 何火娇. 水稻叶片几何参数图像视觉测量方法研究[J]. 湖北农业科学, 2015,54(17):4317-4320. |
YANG H Y, SUN A Z, HE H J. Study on the geometry parameter of rice leaf measuring method using image vision technology[J]. Hubei Agricultural Sciences, 2015,54(17):4317-4320.(in Chinese with English abstract) | |
[19] | 崔世钢, 秦建华, 张永立. 基于图像处理技术的植物叶片面积和周长测量[J]. 江苏农业科学, 2018,46(15):187-189. |
CUI S G, QIN J H, ZHANG Y L. Measurement of leaf area and girth based on image processing technology[J]. Jiangsu Agricultural Sciences, 2018,46(15):187-189.(in Chinese with English abstract) | |
[20] | 吴大伟, 李聪, 徐丽, 等. 基于图像处理技术的植物叶片相关参数测量研究[J].科技信息, 2010(4):106. |
WU D W, LI C, XU L, et al. Measurement of plant leaf parameters based on image processing technology[J]. Science & Technology Information, 2010(4):106. | |
[21] | 李朝东, 崔国贤, 谢宁, 等. 应用数字图像技术诊断苎麻氮素营养的研究简报[J]. 植物营养与肥料学报, 2011,17(3):767-772. |
LI C D, CUI G X, XIE N, et al. Research notes on N status diagnosis of ramie by using digital image technology[J]. Plant Nutrition and Fertilizer Science, 2011,17(3):767-772.(in Chinese with English abstract) | |
[22] | 孙铁波, 王卫兵, 刘春月. 嵌入式水稻氮素营养检测仪的研究与设计[J]. 中国农机化学报, 2015,36(3):257-261. |
SUN T B, WANG W B, LIU C Y. Research and design of the embedded detector for rice nitrogen nutrition[J]. Journal of Chinese Agricultural Mechanization, 2015,36(3):257-261.(in Chinese with English abstract) | |
[23] |
谭昌伟, 周清波, 齐腊, 等. 水稻氮素营养高光谱遥感诊断模型[J]. 应用生态学报, 2008,19(6):1261-1268.
PMID |
TAN C W, ZHOU Q B, QI L, et al. Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status[J]. Chinese Journal of Applied Ecology, 2008,19(6):1261-1268.(in Chinese with English abstract)
URL PMID |
|
[24] | 高创, 郁崇文, 汪军, 等. 棉花性能指标对成纱质量的预测模型研究[J]. 棉纺织技术, 2016,44(6):18-22. |
GAO C, YU C W, WANG J, et al. Prediction model study of cotton quality index on yarn quality[J]. Cotton Textile Technology, 2016,44(6):18-22.(in Chinese with English abstract) | |
[25] | 常军, 李祯, 朱业玉, 等. 基于支持向量机(SVM)方法的冬季温度预测[J]. 气象科技, 2005,33(增刊):100-104. |
CHANG J, LI Z, ZHU Y Y, et al. Application of support vector machine method to winter temperature forecast[J]. Scientia Meteorological Sinica, 2005,33(Suppl.):100-104.(in Chinese with English abstract) | |
[26] | 奉国和. SVM分类核函数及参数选择比较[J]. 计算机工程与应用, 2011,47(3):123-124. |
FENG G H. Parameter optimizing for support vector machines classification[J]. Computer Engineering and Applications, 2011,47(3):123-124.(in Chinese with English abstract) | |
[27] | 周琼, 杨红云, 杨珺, 等. 基于参数优化支持向量机的水稻施氮水平分类研究[J]. 南方农业学报, 2017,48(8):1524-1528. |
ZHOU Q, YANG H Y, YANG J, et al. Classification of nitrogen application level for rice based on support vector machine optimized by parameters[J]. Journal of Southern Agriculture, 2017,48(8):1524-1528.(in Chinese with English abstract) | |
[28] | 胡强, 郝晓燕, 雷蕾. 基于遗传算法和BP神经网络的孤立性肺结节分类算法[J]. 计算机科学, 2016,43(6A):37-39. |
HU Q, HAO X Y, LEI L. Solitary pulmonary nodules classification based on genetic algorithm and back propagation neural networks[J]. Computer Science, 2016,43(6A):37-39.(in Chinese with English abstract) | |
[29] | 师黎, 朱民杰. 基于遗传算法优化BP神经网络在心电图身份识别中的应用[J]. 中国组织工程研究与临床康复, 2010,14(43):8069-8072. |
SHI L, ZHU M J. Human identification using electrocardiograms based on generic algorithm-back propagation neural network[J]. Journal of Clinical Rehabilitative Tissue Engineering Research, 2010,14(43):8069-8072.(in Chinese with English abstract) | |
[30] | 郭士伟, 赵学强, 夏士健, 等. 超级杂交稻生育后期叶片和根系的衰老营养生理研究[J]. 华北农学报, 2014,29(3):115-121. |
GUO S W, ZHAO X Q, XIA S J, et al. The leaf and root nourishment physiology research for the super-hybrid rice after heading[J]. Acta Agriculturae Boreali-Sinica, 2014,29(3):115-121.(in Chinese with English abstract) |
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