番茄病害早期快速诊断与生理信息快速检测方法研究
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摘要
精细农业技术作为农业信息化和农业现代化发展最前沿的领域之一,是当今世界发展现代农业,实现农业可持续发展的关键和核心技术。精细农业要求快速、准确、数字化和定位化的获取农业生产和管理信息,而农作物生长过程信息的快速、准确、动态获取和监控方法和技术需求尤为紧迫。传统的实验室化学测量分析已经不能满足农业对信息快速、准确、动态、高效获取的要求。本研究针对农作物信息快速检测技术的急迫需求,以番茄为研究对象,应用高光谱成像技术系统建立番茄病害的早期诊断识别方法和模型,并实现病害胁迫下番茄叶片生理信息的快速检测,为番茄栽培的精细化管理和病害综合防治提供新的技术支撑,对番茄的精细化生产和种植具有重要意义。本研究主要成果包括：
(1)建立了番茄茎秆灰霉病早期诊断的光谱识别模型和图像识别模型,构建了番茄茎秆病害早期诊断的数据预处理、特征信息提取、线性和非线性识别模型的优化分析路径,为番茄茎秆病害的早期诊断识别提供了有效方法。系统地比较了不同光谱预处理下的全谱偏最小二乘法(PLS)模型,并应用载荷系数法提取特征波长,建立了番茄茎秆灰霉病诊断识别的优化模型。得出的最优光谱判别模型为特征波长-最小二乘-支持向量机(EW-LS-SVM)模型,对预测集样本的正确识别率达到100%。应用概率统计滤波和二阶概率统计滤波提取了高光谱图像的纹理特征信息,建立了PLS判别模型,对预测集样本的正确识别率为97.37%。通过遗传算法-偏最小二乘法(GA-PLS)提取特征纹理,建立了PLS和SVM判别模型,最优模型GA-PLS-PLS模型对预测集样本的准确识别率为92.11%。
(2)建立了番茄叶片三种病害(灰霉病、菌核病和早疫病)胁迫的光谱同步诊断方法和模型。提取了高光谱数据在400-900nnm范围的可见／近红外光谱信息,系统比较了多种光谱预处理方法、PLS和极限学习机(ELM)识别模型,得出的最优模型为全谱ELM模型(Detrending),对预测集样本三种病害同步诊断的正确识别率为94.20%。应用GA-PLS提取的特征波段,并建立了番茄叶片三种病害同步诊断的PLS、误差反向传输神经网络(BPNN)、SVM和ELM模型,总体效果为ELM模型较好,对预测集样本的准确识别率均接近90%。
(3)建立了基于高光谱图像信息的番茄叶片三种病害(灰霉病、菌核病和早疫病)同步诊断模型和两种病害相互识别模型。采用GA-PLS确定特征波长图像,并应用概率统计滤波和二阶概率统计滤波提取纹理特征,系统地比较了PLS、BPNN、SVM和ELM四种建模方法的诊断识别效果。结果表明：对灰霉病、早疫病和健康叶片的诊断识别、以及对灰霉病、菌核病和健康叶片的诊断识别的正确识别率均大于90%；对菌核病、早疫病和健康叶片的诊断识别、以及三种病害同步诊断识别的正确识别率低于80%。
(4)建立了的番茄叶片灰霉病胁迫下过氧化物酶(POD)活力的快速检测模型。比较了全谱PLS和ELM模型对番茄叶片POD的快速检测,最优模型为全谱ELM模型(MSC),对预测集样本预测结果的相关系数r=0.8297,预测集均方根误差RMSEP=983.7830;通过GA-PLS方法,提取了21个番茄叶片POD预测的特征波长,建立了GA-PLS-PLS、GA-PLS-MLR和GA-PLS-ELM模型,得出最优模型为GA-PLS-ELM (SG)模型,对预测集样本的预测结果为r=0.8647,RMSEP=465.9880。结果表明：基于光谱技术进行灰霉病胁迫下番茄叶片POD的快速检测是可行的,为番茄叶片生理指标的动态快速检测提供了新的方法。
Precision agriculture and digital agriculture are the most frontier technologies in modern agriculture, and they are also the key and kernel technologies for the development of modern agriculture and the realization of sustainable agriculture. Precision agriculture and digital agriculture require the fast, accurate, digital and positional agricultural production and management information. However, the traditional lab and chemical measurements cannot fulfill the fast, accurate, dynamic and high efficient demand of modern agriculture. Therefore, this study is mainly focused on the tomato (Lycopersicum esculentum), and aims to develop the fast and accurate disease early diagnosis methods and physiological information detection methods of tomato under different disease stress. This study will supply a new approach for the precision management and disease prevention and treatment of tomato, which is also meaningful for the precision production and plant of tomato. The main results were achieved as follows:
(1) The spectral recognition models and imaging recognition models were developed for the early diagnosis of gray mold disease of tomato stems. An analysis routine was developed for data preprocessing, effective and feature information extraction, linear and nonlinear recognition models, which was effective for the disease early diagnosis of tomato stems. Different spectral preprocessing methods were compared for full-spectrum partial least squares (PLS) models. Effective wavelengths (EWs) were selected by loading weights and used as input of recognition models. The optimal recognition ratio was achieved by effective wavelength-least squares-support vector machine (EW-LS-SVM) model with a correct recognition ratio of100%for validation set. Probability statistics filter and2-deravertive probability statistics filter were used for texture feature extraction of hyper-spectral imaging information. The PLS model obtained a correct recognition ratio of97.37%for validation set. Genetic algorithm-partial least squares (GA-PLS) method was applied to extract the effective texture features, and PLS and SVM models were developed for gray mold disease diagnosis of tomato stems. The optimal model was GA-PLS-PLS model and the correct recognition ratio was92.11%for validation set.
(2) A synchronous diagnosis model using spectral information was developed for three disease of tomato, including gray mold disease, sclerotinia sclerotiorum disease and early blight disease. The visible and near infrared (400-900nm) spectral information was extracted for hyper-spectral imaging data, and different spectral preprocessing methods were compared for the development of PLS and extreme learning machine (ELM) models. The optimal model was full-spectrum ELM model (Detrending) with a correct recognition ratio of94.20%for synchronous diagnosis of three diseases. GA-PLS was applied for EW selection, and then PLS, back propagation neural networks (BPNN), SVM and ELM models were developed for disease diagnosis. The optimal results were achieved by ELM model with a correct recognition ratio near90%for validation set.
(3) A synchronous diagnosis methods for three diseases and each of two kinds of diseases were developed using hyper-spectral imaging information. GA-PLS was used to settle the effective wavelength, and the corresponding imaging of these effective wavelengths were selected for texture feature extraction using probability statistics filter and2-deravertive probability statistics filter. Four kinds of models were developed for disease recognition, including PLS, BPNN, SVM and ELM. The results indicated that the correct recognition ratio for the combination of gray mold, early blight diseases and healthy leaves, and the combination of gray mold, sclerotinia sclerotiorum diseases and healthy leaves were over90%, which were acceptable results. However, the recognition ratio for the combination of sclerotinia sclerotiorum, early blight diseases and healthy leaves, the combination of these three diseases and healthy leaves were both less than80%.
(4) The physiological information of peroxidase (POD) of tomato leaves under gray mold disease stress was determined using spectral technology. A comparison of full-spectrum PLS and ELM models were proceeded, and the results indicated that full-spectrum ELM model (MSC) achieved a better prediction performance with r=0.8297and root mean squares error of prediction (RMSEP)=983.7830for validation set. Moreover, GA-PLS was used to extract effective wavelengths, and21EWs were selected to develop GA-PLS-PLS, GA-PLS-MLR, GA-PLS-ELM models to determine POD of tomato leaves. The results indicated that GA-PLS-ELM model (SG) achieved an optimal prediction performance with r=0.8647and RMSEP=465.9880for validation set, which is also the best result for all developed models. The overall results demonstrated that it was feasible to detect the POD of tomato leaves under gray mold disease stress using spectral technology, which supplied a new approach for physiological parameter detection of tomato leaves.
(1)建立了番茄茎秆灰霉病早期诊断的光谱识别模型和图像识别模型,构建了番茄茎秆病害早期诊断的数据预处理、特征信息提取、线性和非线性识别模型的优化分析路径,为番茄茎秆病害的早期诊断识别提供了有效方法。系统地比较了不同光谱预处理下的全谱偏最小二乘法(PLS)模型,并应用载荷系数法提取特征波长,建立了番茄茎秆灰霉病诊断识别的优化模型。得出的最优光谱判别模型为特征波长-最小二乘-支持向量机(EW-LS-SVM)模型,对预测集样本的正确识别率达到100%。应用概率统计滤波和二阶概率统计滤波提取了高光谱图像的纹理特征信息,建立了PLS判别模型,对预测集样本的正确识别率为97.37%。通过遗传算法-偏最小二乘法(GA-PLS)提取特征纹理,建立了PLS和SVM判别模型,最优模型GA-PLS-PLS模型对预测集样本的准确识别率为92.11%。
(2)建立了番茄叶片三种病害(灰霉病、菌核病和早疫病)胁迫的光谱同步诊断方法和模型。提取了高光谱数据在400-900nnm范围的可见／近红外光谱信息,系统比较了多种光谱预处理方法、PLS和极限学习机(ELM)识别模型,得出的最优模型为全谱ELM模型(Detrending),对预测集样本三种病害同步诊断的正确识别率为94.20%。应用GA-PLS提取的特征波段,并建立了番茄叶片三种病害同步诊断的PLS、误差反向传输神经网络(BPNN)、SVM和ELM模型,总体效果为ELM模型较好,对预测集样本的准确识别率均接近90%。
(3)建立了基于高光谱图像信息的番茄叶片三种病害(灰霉病、菌核病和早疫病)同步诊断模型和两种病害相互识别模型。采用GA-PLS确定特征波长图像,并应用概率统计滤波和二阶概率统计滤波提取纹理特征,系统地比较了PLS、BPNN、SVM和ELM四种建模方法的诊断识别效果。结果表明：对灰霉病、早疫病和健康叶片的诊断识别、以及对灰霉病、菌核病和健康叶片的诊断识别的正确识别率均大于90%；对菌核病、早疫病和健康叶片的诊断识别、以及三种病害同步诊断识别的正确识别率低于80%。
(4)建立了的番茄叶片灰霉病胁迫下过氧化物酶(POD)活力的快速检测模型。比较了全谱PLS和ELM模型对番茄叶片POD的快速检测,最优模型为全谱ELM模型(MSC),对预测集样本预测结果的相关系数r=0.8297,预测集均方根误差RMSEP=983.7830;通过GA-PLS方法,提取了21个番茄叶片POD预测的特征波长,建立了GA-PLS-PLS、GA-PLS-MLR和GA-PLS-ELM模型,得出最优模型为GA-PLS-ELM (SG)模型,对预测集样本的预测结果为r=0.8647,RMSEP=465.9880。结果表明：基于光谱技术进行灰霉病胁迫下番茄叶片POD的快速检测是可行的,为番茄叶片生理指标的动态快速检测提供了新的方法。
Precision agriculture and digital agriculture are the most frontier technologies in modern agriculture, and they are also the key and kernel technologies for the development of modern agriculture and the realization of sustainable agriculture. Precision agriculture and digital agriculture require the fast, accurate, digital and positional agricultural production and management information. However, the traditional lab and chemical measurements cannot fulfill the fast, accurate, dynamic and high efficient demand of modern agriculture. Therefore, this study is mainly focused on the tomato (Lycopersicum esculentum), and aims to develop the fast and accurate disease early diagnosis methods and physiological information detection methods of tomato under different disease stress. This study will supply a new approach for the precision management and disease prevention and treatment of tomato, which is also meaningful for the precision production and plant of tomato. The main results were achieved as follows:
(1) The spectral recognition models and imaging recognition models were developed for the early diagnosis of gray mold disease of tomato stems. An analysis routine was developed for data preprocessing, effective and feature information extraction, linear and nonlinear recognition models, which was effective for the disease early diagnosis of tomato stems. Different spectral preprocessing methods were compared for full-spectrum partial least squares (PLS) models. Effective wavelengths (EWs) were selected by loading weights and used as input of recognition models. The optimal recognition ratio was achieved by effective wavelength-least squares-support vector machine (EW-LS-SVM) model with a correct recognition ratio of100%for validation set. Probability statistics filter and2-deravertive probability statistics filter were used for texture feature extraction of hyper-spectral imaging information. The PLS model obtained a correct recognition ratio of97.37%for validation set. Genetic algorithm-partial least squares (GA-PLS) method was applied to extract the effective texture features, and PLS and SVM models were developed for gray mold disease diagnosis of tomato stems. The optimal model was GA-PLS-PLS model and the correct recognition ratio was92.11%for validation set.
(2) A synchronous diagnosis model using spectral information was developed for three disease of tomato, including gray mold disease, sclerotinia sclerotiorum disease and early blight disease. The visible and near infrared (400-900nm) spectral information was extracted for hyper-spectral imaging data, and different spectral preprocessing methods were compared for the development of PLS and extreme learning machine (ELM) models. The optimal model was full-spectrum ELM model (Detrending) with a correct recognition ratio of94.20%for synchronous diagnosis of three diseases. GA-PLS was applied for EW selection, and then PLS, back propagation neural networks (BPNN), SVM and ELM models were developed for disease diagnosis. The optimal results were achieved by ELM model with a correct recognition ratio near90%for validation set.
(3) A synchronous diagnosis methods for three diseases and each of two kinds of diseases were developed using hyper-spectral imaging information. GA-PLS was used to settle the effective wavelength, and the corresponding imaging of these effective wavelengths were selected for texture feature extraction using probability statistics filter and2-deravertive probability statistics filter. Four kinds of models were developed for disease recognition, including PLS, BPNN, SVM and ELM. The results indicated that the correct recognition ratio for the combination of gray mold, early blight diseases and healthy leaves, and the combination of gray mold, sclerotinia sclerotiorum diseases and healthy leaves were over90%, which were acceptable results. However, the recognition ratio for the combination of sclerotinia sclerotiorum, early blight diseases and healthy leaves, the combination of these three diseases and healthy leaves were both less than80%.
(4) The physiological information of peroxidase (POD) of tomato leaves under gray mold disease stress was determined using spectral technology. A comparison of full-spectrum PLS and ELM models were proceeded, and the results indicated that full-spectrum ELM model (MSC) achieved a better prediction performance with r=0.8297and root mean squares error of prediction (RMSEP)=983.7830for validation set. Moreover, GA-PLS was used to extract effective wavelengths, and21EWs were selected to develop GA-PLS-PLS, GA-PLS-MLR, GA-PLS-ELM models to determine POD of tomato leaves. The results indicated that GA-PLS-ELM model (SG) achieved an optimal prediction performance with r=0.8647and RMSEP=465.9880for validation set, which is also the best result for all developed models. The overall results demonstrated that it was feasible to detect the POD of tomato leaves under gray mold disease stress using spectral technology, which supplied a new approach for physiological parameter detection of tomato leaves.
引文
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