基于多特征和改进BPNN的降香黄檀冠层叶片全氮含量无损诊断
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摘要
氮素是植物生命活动不可或缺的营养成分之一,合理的施肥不仅有利于植物健康成长,还可以减少对土壤及地下水污染等生态问题。本研究以降香黄檀为对象,提出了一种基于多特征和改进BPNN(Back Propagation Neural Network)的冠层叶片全氮含量无损诊断方法。通过数字图像处理技术对冠层图像进行分割,计算得到27种图像特征(颜色、纹理、形状),通过计算Pearson系数筛选出与全氮含量显著相关的因子,做主成分分析并提取前四主成分,将其作为改进BPNN(即PSO-BPNN-Adaboost)的输入向量。结果表明:使用多特征能更全面准确地反映降香黄檀冠层叶片的全氮含量;另一方面,BPNN、PSO-BPNN、BPNN-Adaboost与PSO-BPNN-Adaboost算法的比较结果表明,PSO-BPNN-Adaboost算法更可靠;同时,PSO处理对结果的优化程度更大,因此对于BP神经网络来说,需要先找一个合适的初始值和阈值,再对其进行增强处理。本研究考虑了氮胁迫对植株多方面的影响,突破了仅限于从颜色角度实现全氮含量预测的方法,也为珍贵树种经营中"精准施肥"提供了参考,可有效减小使用过量肥料造成的生态污染等问题。
Nitrogen is one of the indispensable nutritional components for plant growth. Proper fertilization is not only conducive to the healthy growth of plants,but also can reduce the pollution of soil and groundwater. In this study,we proposed a nondestructive diagnosis method of total nitrogen content in canopy leaves of Dalbergia odorifera based on multi-feature and improved BPNN. We divided the canopy image by digital image processing technology,and obtained 27 image features( color,texture,and shape). By calculating the Pearson coefficient,we screened out the factors significantly related to total nitrogen content by principal components analysis. We extracted the first four principal components as the input factors of improved BPNN which was optimized by PSO and Adaboost algorithm( PSO-BPNN-Adaboost). The results showed that the use of multiple features can more comprehensively and accurately reflect the nitrogen content of canopy leaves in Dalbergia odorifera. By comparing BPNN,PSO-BPNN,BPNN-Adaboost and PSOBPNN-Adaboost,we found that the PSO-BPNN-Adaboost was more reliable and the PSO algorithm had a more optimization effect. Therefore,for the BP neural network,it is necessary to find a suitable initial value and threshold before enhancing them. Our method fully considered the influence of nitrogen stress on plants and outcompeted the traditional method which only considered color factors. Our method provided reference for the precision fertilization in the management of precious trees,which could effectively reduce environmental pollution.
Nitrogen is one of the indispensable nutritional components for plant growth. Proper fertilization is not only conducive to the healthy growth of plants,but also can reduce the pollution of soil and groundwater. In this study,we proposed a nondestructive diagnosis method of total nitrogen content in canopy leaves of Dalbergia odorifera based on multi-feature and improved BPNN. We divided the canopy image by digital image processing technology,and obtained 27 image features( color,texture,and shape). By calculating the Pearson coefficient,we screened out the factors significantly related to total nitrogen content by principal components analysis. We extracted the first four principal components as the input factors of improved BPNN which was optimized by PSO and Adaboost algorithm( PSO-BPNN-Adaboost). The results showed that the use of multiple features can more comprehensively and accurately reflect the nitrogen content of canopy leaves in Dalbergia odorifera. By comparing BPNN,PSO-BPNN,BPNN-Adaboost and PSOBPNN-Adaboost,we found that the PSO-BPNN-Adaboost was more reliable and the PSO algorithm had a more optimization effect. Therefore,for the BP neural network,it is necessary to find a suitable initial value and threshold before enhancing them. Our method fully considered the influence of nitrogen stress on plants and outcompeted the traditional method which only considered color factors. Our method provided reference for the precision fertilization in the management of precious trees,which could effectively reduce environmental pollution.
引文
陈永喆,傅伯杰,冯晓明.2017.遥感反演植被氮含量研究进展.生态学报,37(18):6240-6252.
郭澎涛,苏艺,茶正早,等.2016.基于BP神经网络的橡胶苗叶片磷含量高光谱预测.农业工程学报,32(s1):177-183.
贺付亮,郭永彩,高潮,等.2017.基于视觉显著性和脉冲耦合神经网络的成熟桑葚图像分割.农业工程学报,33(6):148-155.
刘国海,肖夏宏,江辉,等.2013.基于BP-Adaboost的近红外光谱检测固态发酵过程p H值.江苏大学学报:自然科学版,34(5):574-578.
李荣丽,陈志彪,陈志强,等.2015.基于BP神经网络的流域生态恢复度计算---以福建长汀朱溪小流域为例生态学报,35(6):1973-1981.
孟慧,杨云,冯锦东.2010.降香黄檀引种栽培现状与发展.广东农业科学,37(7):79-80.
宋述尧,王秀峰.2008.数字图像技术在黄瓜氮素营养诊断上的应用研究.吉林农业大学学报,30(4):460-465.
王军,费凯,程勇.2017.基于改进的Adaboost-BP模型在降水中的预测.计算机应用,37(9):2689-2693.
王楠,王宏信,李向林,等.2017.施肥对降香黄檀幼苗生长和光合的影响.东北林业大学学报,45(1):25-29.
魏全全,李岚涛,任涛,等.2015.基于数字图像技术的冬油菜氮素营养诊断.中国农业科学,48(19):3877-3886.
余彬,万燕珍,陈思超,等.2017.基于密度相似因子的电力红外图像分割方法.红外技术,(12):1139-1143.
尹怀仙,王凯,张铁柱,等.2015.基于PSO-BP神经网络的城轨列车转向架轮对轴箱故障预测.复杂系统与复杂性科学,12(4):97-103.
杨菁,周国英,田媛媛,等.2015.降香黄檀不同混交林土壤细菌多样性差异分析.生态学报,35(24):8117-8127.
杨权,刘君昂,吴毅,等.2016.降香黄檀林下伴生种适应性评价.中南林业科技大学学报,36(8):33-38.
郑坚,吴朝辉,陈秋夏,等.2016.遮荫对降香黄檀幼苗生长和生理的影响.林业科学,52(12):50-57.
赵霞,徐大平,杨曾奖,等.2017.养分胁迫对降香黄檀幼苗生长及叶片养分状况的影响.生态学杂志,36(6):1503-1508.
张亦涛,刘宏斌,王洪媛,等.2016.农田施氮对水质和氮素流失的影响.生态学报,36(20):6664-6676.
Cameron KC,Di HJ,Moir JL.2013.Nitrogen losses from the soil/plant system:A review.Annals of Applied Biology,162:145-173.
Confalonieri R,Paleari L,Movedi E,et al.2015.Improving in vivo plant nitrogen content estimates from digital images:Trueness and precision of a new approach as compared to other methods and commercial devices.Biosystems Engineering,135:21-30.
Lin FF,Qiu LF,Deng JS,et al.2010.Investigation of SPADmeter-based indices for estimating rice nitrogen status.Computers and Electronics in Agriculture,71:60-65.
Mao HP,Gao HY,Zhang XD,et al.2015.Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision.Scientia Horticulturae,184:1-7.
Romualdo LM,Luz PHC,Devechio FFS,et al.2014.Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants.Computers and Electronics in Agriculture,104:63-70.
Yu XJ,Lu HD,Liu QY.2018.Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape(Brassica napus L.)leaf.Chemometrics and Intelligent Laboratory Systems,172:188-193.
郭澎涛,苏艺,茶正早,等.2016.基于BP神经网络的橡胶苗叶片磷含量高光谱预测.农业工程学报,32(s1):177-183.
贺付亮,郭永彩,高潮,等.2017.基于视觉显著性和脉冲耦合神经网络的成熟桑葚图像分割.农业工程学报,33(6):148-155.
刘国海,肖夏宏,江辉,等.2013.基于BP-Adaboost的近红外光谱检测固态发酵过程p H值.江苏大学学报:自然科学版,34(5):574-578.
李荣丽,陈志彪,陈志强,等.2015.基于BP神经网络的流域生态恢复度计算---以福建长汀朱溪小流域为例生态学报,35(6):1973-1981.
孟慧,杨云,冯锦东.2010.降香黄檀引种栽培现状与发展.广东农业科学,37(7):79-80.
宋述尧,王秀峰.2008.数字图像技术在黄瓜氮素营养诊断上的应用研究.吉林农业大学学报,30(4):460-465.
王军,费凯,程勇.2017.基于改进的Adaboost-BP模型在降水中的预测.计算机应用,37(9):2689-2693.
王楠,王宏信,李向林,等.2017.施肥对降香黄檀幼苗生长和光合的影响.东北林业大学学报,45(1):25-29.
魏全全,李岚涛,任涛,等.2015.基于数字图像技术的冬油菜氮素营养诊断.中国农业科学,48(19):3877-3886.
余彬,万燕珍,陈思超,等.2017.基于密度相似因子的电力红外图像分割方法.红外技术,(12):1139-1143.
尹怀仙,王凯,张铁柱,等.2015.基于PSO-BP神经网络的城轨列车转向架轮对轴箱故障预测.复杂系统与复杂性科学,12(4):97-103.
杨菁,周国英,田媛媛,等.2015.降香黄檀不同混交林土壤细菌多样性差异分析.生态学报,35(24):8117-8127.
杨权,刘君昂,吴毅,等.2016.降香黄檀林下伴生种适应性评价.中南林业科技大学学报,36(8):33-38.
郑坚,吴朝辉,陈秋夏,等.2016.遮荫对降香黄檀幼苗生长和生理的影响.林业科学,52(12):50-57.
赵霞,徐大平,杨曾奖,等.2017.养分胁迫对降香黄檀幼苗生长及叶片养分状况的影响.生态学杂志,36(6):1503-1508.
张亦涛,刘宏斌,王洪媛,等.2016.农田施氮对水质和氮素流失的影响.生态学报,36(20):6664-6676.
Cameron KC,Di HJ,Moir JL.2013.Nitrogen losses from the soil/plant system:A review.Annals of Applied Biology,162:145-173.
Confalonieri R,Paleari L,Movedi E,et al.2015.Improving in vivo plant nitrogen content estimates from digital images:Trueness and precision of a new approach as compared to other methods and commercial devices.Biosystems Engineering,135:21-30.
Lin FF,Qiu LF,Deng JS,et al.2010.Investigation of SPADmeter-based indices for estimating rice nitrogen status.Computers and Electronics in Agriculture,71:60-65.
Mao HP,Gao HY,Zhang XD,et al.2015.Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision.Scientia Horticulturae,184:1-7.
Romualdo LM,Luz PHC,Devechio FFS,et al.2014.Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants.Computers and Electronics in Agriculture,104:63-70.
Yu XJ,Lu HD,Liu QY.2018.Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape(Brassica napus L.)leaf.Chemometrics and Intelligent Laboratory Systems,172:188-193.