基于SVM和GIS的梨小食心虫预测系统的研究
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摘要
果树病虫害防治尤需精准的预测预报技术,目前生产上主要采取经验式防治,容易造成防治不及时,导致果实产量和品质下降。为了提高果树害虫的预测准确率,本文以梨小食心虫为研究对象,以气象因子为主要影响因子,运用相关分析、专家知识进行关键影响因子的筛选,应用基于统计学习理论的支持向量机(SVM)构建梨小食心虫发生期、发生程度的预测模型,探索梨小食心虫高效的预测方法。集成多种信息技术,设计并实现了基于支持向量机和GIS的梨小食心虫预测系统,为其它果树害虫的预测提供参考方法与技术平台。主要研究结论如下:
1.提出了梨小食心虫预测每个环节所应用的相关方法及其流程,为提高预测准确率奠定了基础。针对以往将气象数据分隔成旬、月因子进行分析存在的不足,本文气象数据处理上采用“膨化处理”方法,结果表明因子量显著增多,克服了梨小食心虫由于各代间隔时间短而难以筛选出显著相关因子的困难;同时气象因子连续性与累积性的特点也得以充分体现,有利于筛选出更符合梨小食心虫生物学规律的因子。针对“膨化处理”后显著相关因子多的特点,对筛选出的气象因子明确规定了用生物学规律、相关系数大小、时间段等作为因子的入选标准,改变了以往单独用显著性水平作为因子的入选标准,提高了因子的选择效率。
2.将相关分析与专家知识结合进行梨小食心虫发蛾高峰期、发生程度关键影响因子的筛选、确定。结果表明筛选出的因子多数具有时间上的连续性,客观地反映了梨小食心虫发生发展连续性的特点。发蛾高峰期方面,该虫不同代的关键影响因子不同,影响因子超出了以往温度、湿度的范围,其中温度是共性的影响因子,成反比关系;湿度因子影响第二代至第四代的发蛾高峰期,成正比关系;此外,最低温、与降水有关的因子还影响部分代的发蛾高峰期。发生程度方面,筛选出的影响因子多,主要是湿度以及与降水有关的因子,成正比关系,但秋冬季节的湿度、降水等因子与发生程度成反比关系;另外温度、日照等因子也影响部分代的发生程度。影响因子的筛选定量地描述了梨小食心虫不同代的发生发展与对应时段气象因子的相关程度。
3.基于支持向量机回归、分类分别建立了梨小食心虫越冬代至第四代发蛾高峰期、发生程度的预测模型;通过参数选择,优化了模型,提高了预测准确率;并与BP神经网络模型进行了分析比较。结果表明基于支持向量机构建的梨小食心虫各代发蛾高峰期模型的预测准确率高,平均预测准确率达93.6%,较BP神经网络的平均预测准确率82.7%高10.9%。基于支持向量机构建的梨小食心虫各代发生程度模型的平均预测准确率为82.0%,显著高于BP神经网络模型的平均预测准确率63.2%。支持向量机模型的发蛾高峰期、发生程度的均方误差值小于BP神经网络。预测结果表明梨小食心虫发蛾高峰期、发生程度的支持向量机模型均比对应的BP神经网络模型的预测准确率高且稳定性强。
4.以MapObjects和C#.NET为开发工具,构建了基于支持向量机和GIS的梨小食心虫预测系统。该系统具有数据管理、查询、统计、预测预报、防治决策、专题图制作、信息发布等功能。系统中应用支持向量机进行梨小食心虫预测,提高了预测准确率,克服了以往系统中用BP神经网络进行预测存在的不足。该系统具有良好的扩充性,能直接用于其它果树害虫的预测,可作为果树害虫预测与信息管理的技术平台。
Precise predicting and forecasting methods are necessary in fruit pest and disease control. At present, practical experience control is mainly used in the fruit production, resulting in delays in the treatment procedures and a decrease of fruit production and quality. In order to improve the accuracy of prediction, Grapholitha molesta (Busck) was selected as a study object, the key affecting factors were screened out by correlation analysis and expert knowledge, according to the meteological factors. Prediction models of the adult peak period and the occurrence degree of G molesta were established by Support Vector Machine (SVM), based on the statistical learning theory, in order to provide an efficient prediction method. Finally, a prediction system of G molesta was designed and developed based on SVM and Geographic Information System (GIS) through the integration of poly-information technology, providing a prediction reference method and technology platform for other fruit pests. The main results obtained were as follows:
1. The prediction accuracy was ensured by proposing the methods and processes in each prediction link of G. molesta. Considering the shortage of previous methods of analysis, in which the meteorological data was separated by 10-day periods or months, puffing treatment technology was applied to deal with meteorological factors, and as a result, the factor number increased evidently. Consequently, the difficulty of screening the affecting factors due to the short interval of the pest's occurrence was overcome successfully. The continuity and accumulates of the meteorological factors were also manifested fully, which will be helpful to screen out the factors more satisfied with the biological rules of G. molesta. To avoid the significant level as the unique screening standard, the biological rule, correlation coefficient and the period of the meteorological factors were used as the screening standards to select the relative factors. Therefore, the screening efficiency of the affecting factors was increased.
2. The key affecting factors of the adult peak period and occurrence degree of G. molesta were selected and confirmed by combining the mathematical statistics method and expert knowledge. The continuity of most selected factors demonstrated the continuity of occurrence and development of G. molesta. For the adult peak period, the affecting factors of different generations were varied. The affecting factors were not limited to the range of temperature and humidity. Temperature, a co-factor, was inversely related to the adult peak period, and humidity, a proportional factor, turned out to be the key affecting factor of the adult peak period from 2nd to 4th generation. At the same time, low temperature and rainfall had an effect on adult peak period of some generations. For the occurrence degree, the affecting factors, mostly related with humidity or rainfall, were proportional; the more rainfall or the higher of humility, the higher occurrence of G. molesta. Nevertheless, in fall and winter, the influence of humility and rainfall were inverse with the occurrence degree. In addition, temperature and sunshine affected the occurrence degree of some generations. The relationship of occurrence and development of G. molesta with the mathematical factors in corresponding periods were described quantitatively through the screened affecting factors.
3. Based on the regression and classification of LibSVM, the models of adult peak period and occurrence degree of G molesta from the overwintering generation to 4th generation were established. Through the parameters selected, the models were optimized, and therefore, the accuracy of the prediction was enhanced. The results were compared with the models established by BP Neural Networks. The accuracy of the prediction model of the adult peak period for each generation based on SVM was 93.6% on average, about 10.9% higher than that by BP Neural Networks (82.7% on average). Similarly, the accuracy of the prediction model of the occurrence degree for each generation, established by SVM (82% on average) was significantly higher than that by BP Neural Networks (63.2% on average). The mean square error of the model of SVM was less than that of BP Neural Networks. In conclusion, the models based on SVM could predict more precisely and stably than that of BP Neural Networks.
4. The prediction system of G. molesta based on SVM and GIS was established by the development platform of MapObjects and C#.NET. The functions of data management, inquiry, statistics, prediction and forecasting, control decision, thematic map development, information publishing etc. were included. The accuracy of prediction was increased by using the SVM, and the system overcame the shortage of BP Neural Networks. This system could be extended to predict other pests and provide a technology platform of prediction and information management of fruit pests.
1.提出了梨小食心虫预测每个环节所应用的相关方法及其流程,为提高预测准确率奠定了基础。针对以往将气象数据分隔成旬、月因子进行分析存在的不足,本文气象数据处理上采用“膨化处理”方法,结果表明因子量显著增多,克服了梨小食心虫由于各代间隔时间短而难以筛选出显著相关因子的困难;同时气象因子连续性与累积性的特点也得以充分体现,有利于筛选出更符合梨小食心虫生物学规律的因子。针对“膨化处理”后显著相关因子多的特点,对筛选出的气象因子明确规定了用生物学规律、相关系数大小、时间段等作为因子的入选标准,改变了以往单独用显著性水平作为因子的入选标准,提高了因子的选择效率。
2.将相关分析与专家知识结合进行梨小食心虫发蛾高峰期、发生程度关键影响因子的筛选、确定。结果表明筛选出的因子多数具有时间上的连续性,客观地反映了梨小食心虫发生发展连续性的特点。发蛾高峰期方面,该虫不同代的关键影响因子不同,影响因子超出了以往温度、湿度的范围,其中温度是共性的影响因子,成反比关系;湿度因子影响第二代至第四代的发蛾高峰期,成正比关系;此外,最低温、与降水有关的因子还影响部分代的发蛾高峰期。发生程度方面,筛选出的影响因子多,主要是湿度以及与降水有关的因子,成正比关系,但秋冬季节的湿度、降水等因子与发生程度成反比关系;另外温度、日照等因子也影响部分代的发生程度。影响因子的筛选定量地描述了梨小食心虫不同代的发生发展与对应时段气象因子的相关程度。
3.基于支持向量机回归、分类分别建立了梨小食心虫越冬代至第四代发蛾高峰期、发生程度的预测模型;通过参数选择,优化了模型,提高了预测准确率;并与BP神经网络模型进行了分析比较。结果表明基于支持向量机构建的梨小食心虫各代发蛾高峰期模型的预测准确率高,平均预测准确率达93.6%,较BP神经网络的平均预测准确率82.7%高10.9%。基于支持向量机构建的梨小食心虫各代发生程度模型的平均预测准确率为82.0%,显著高于BP神经网络模型的平均预测准确率63.2%。支持向量机模型的发蛾高峰期、发生程度的均方误差值小于BP神经网络。预测结果表明梨小食心虫发蛾高峰期、发生程度的支持向量机模型均比对应的BP神经网络模型的预测准确率高且稳定性强。
4.以MapObjects和C#.NET为开发工具,构建了基于支持向量机和GIS的梨小食心虫预测系统。该系统具有数据管理、查询、统计、预测预报、防治决策、专题图制作、信息发布等功能。系统中应用支持向量机进行梨小食心虫预测,提高了预测准确率,克服了以往系统中用BP神经网络进行预测存在的不足。该系统具有良好的扩充性,能直接用于其它果树害虫的预测,可作为果树害虫预测与信息管理的技术平台。
Precise predicting and forecasting methods are necessary in fruit pest and disease control. At present, practical experience control is mainly used in the fruit production, resulting in delays in the treatment procedures and a decrease of fruit production and quality. In order to improve the accuracy of prediction, Grapholitha molesta (Busck) was selected as a study object, the key affecting factors were screened out by correlation analysis and expert knowledge, according to the meteological factors. Prediction models of the adult peak period and the occurrence degree of G molesta were established by Support Vector Machine (SVM), based on the statistical learning theory, in order to provide an efficient prediction method. Finally, a prediction system of G molesta was designed and developed based on SVM and Geographic Information System (GIS) through the integration of poly-information technology, providing a prediction reference method and technology platform for other fruit pests. The main results obtained were as follows:
1. The prediction accuracy was ensured by proposing the methods and processes in each prediction link of G. molesta. Considering the shortage of previous methods of analysis, in which the meteorological data was separated by 10-day periods or months, puffing treatment technology was applied to deal with meteorological factors, and as a result, the factor number increased evidently. Consequently, the difficulty of screening the affecting factors due to the short interval of the pest's occurrence was overcome successfully. The continuity and accumulates of the meteorological factors were also manifested fully, which will be helpful to screen out the factors more satisfied with the biological rules of G. molesta. To avoid the significant level as the unique screening standard, the biological rule, correlation coefficient and the period of the meteorological factors were used as the screening standards to select the relative factors. Therefore, the screening efficiency of the affecting factors was increased.
2. The key affecting factors of the adult peak period and occurrence degree of G. molesta were selected and confirmed by combining the mathematical statistics method and expert knowledge. The continuity of most selected factors demonstrated the continuity of occurrence and development of G. molesta. For the adult peak period, the affecting factors of different generations were varied. The affecting factors were not limited to the range of temperature and humidity. Temperature, a co-factor, was inversely related to the adult peak period, and humidity, a proportional factor, turned out to be the key affecting factor of the adult peak period from 2nd to 4th generation. At the same time, low temperature and rainfall had an effect on adult peak period of some generations. For the occurrence degree, the affecting factors, mostly related with humidity or rainfall, were proportional; the more rainfall or the higher of humility, the higher occurrence of G. molesta. Nevertheless, in fall and winter, the influence of humility and rainfall were inverse with the occurrence degree. In addition, temperature and sunshine affected the occurrence degree of some generations. The relationship of occurrence and development of G. molesta with the mathematical factors in corresponding periods were described quantitatively through the screened affecting factors.
3. Based on the regression and classification of LibSVM, the models of adult peak period and occurrence degree of G molesta from the overwintering generation to 4th generation were established. Through the parameters selected, the models were optimized, and therefore, the accuracy of the prediction was enhanced. The results were compared with the models established by BP Neural Networks. The accuracy of the prediction model of the adult peak period for each generation based on SVM was 93.6% on average, about 10.9% higher than that by BP Neural Networks (82.7% on average). Similarly, the accuracy of the prediction model of the occurrence degree for each generation, established by SVM (82% on average) was significantly higher than that by BP Neural Networks (63.2% on average). The mean square error of the model of SVM was less than that of BP Neural Networks. In conclusion, the models based on SVM could predict more precisely and stably than that of BP Neural Networks.
4. The prediction system of G. molesta based on SVM and GIS was established by the development platform of MapObjects and C#.NET. The functions of data management, inquiry, statistics, prediction and forecasting, control decision, thematic map development, information publishing etc. were included. The accuracy of prediction was increased by using the SVM, and the system overcame the shortage of BP Neural Networks. This system could be extended to predict other pests and provide a technology platform of prediction and information management of fruit pests.
引文
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