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
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摘要
【目的】实现图像氮素营养诊断需要关键指标的确定和建立快速处理海量图像数据的模型。本研究筛选了水稻氮素营养诊断的敏感时期和部位,优化了图像处理技术参数,并比较了BP神经网络和概率神经网络两种建模方法对养分诊断的可靠性,为利用计算机视觉虚拟技术快速精准判断作物生长营养状况、反演生长过程提供思路和方法。【方法】本研究以超级杂交稻‘两优培九’为试验对象进行了田间试验。设置4个施氮(N)水平:0、210、300、390 kg/hm~2。在水稻幼穗分化期及齐穗期,扫描获取水稻顶一叶、顶二叶、顶三叶叶片、叶鞘图像数据,共1920组。通过图像处理技术,获取19项水稻特征指标。分别应用BP神经网络和概率神经网络对19项水稻特征指标进行水稻氮素营养诊断识别,并对诊断指标进行了优化和标准化。比较了两个建模方法的灵敏性。【结果】1)幼穗分化期水稻的整体识别准确率均高于齐穗期水稻的整体识别准确率;三个部位叶片的图像数据,以顶三叶最为可靠;2) BP神经网络对幼穗分化期及齐穗期水稻19项特征指标进行氮素营养诊断的整体识别准确率均高于概率神经网络。其中BP神经网络对幼穗分化期顶三叶特征指标进行水稻氮素营养诊断识别的准确率最高达90%。概率神经网络对幼穗分化期顶二叶、顶三叶特征指标进行水稻氮素营养诊断识别的准确率最高达82%。【结论】幼穗分化期水稻顶3叶叶片特征最具区分度,易于进行氮素营养诊断识别,可作为氮素营养诊断的有效时期和部位。叶片的6项RGB、HSI颜色空间分量组合最能体现其氮素营养状况。识别效果以BP神经网络好于概率神经网络方法,其整体识别准确率达90%。
【Objectives】Identifying key indicators for image and establishing models for processing of massive image data rapidly were two main steps in achieving nitrogen nutrition diagnosis. This project screened the sensitive period and location of rice nitrogen nutrition diagnosis, optimized the image processing technical parameters, and compared the reliability between two methods, BP neural network and probabilistic neural network, in nutrient diagnosis, which provided ideas and methods for determining the nutritional status of crops and inverting the growth process quickly and accurately by using computer vision virtual technology.【Methods】In this study, super hybrid rice ‘LYP9’ was used as experimental crop to set up four kinds of rice cultivation experiments at different fertilization levels(equivalent to N 0, 210, 300 and 390 kg/hm~2), the image data of a total of 1920 groups of the first leaves and the second leaves, the third leaves from crop top, and their corresponding sheaths were obtained by scanning with a scanner during the panicle differentiation stage and the full heading stage. Nineteen rice indexes were obtained. The diagnosis models of rice nitrogen nutrition on standardized nineteen rice characteristic indexes obtained by image processing were respectively established by applying BP neural network and probabilistic neural network. Based on the models, rice nitrogen nutrition diagnosis and identification were carried out. 【Results】1) The accuracy of overall recognition of rice at the panicle differentiation stage was higher than that of the full heading stage; the image data of the third leaves was the most reliable among the three parts of leaves; 2) For the panicle differentiation stage and the full heading stage, the overall recognition accuracy of the nineteen rice characteristic indexes obtained by image processing was higher by using the BP neural network, compared with the probabilistic neural network. The accuracy of diagnosis and identification of rice nitrogen nutrition in the nineteen rice characteristic indexes of the third leaves at the panicle differentiation stage processed through the BP neural network was up to 90%. The accuracy of diagnosis and identification of rice nitrogen nutrition in the nineteen rice characteristic indexes of the second leaves and the third leaves at the panicle differentiation stage processed through the probabilistic neural network was up to 82%. 【Conclusions】The leaf characteristics of the third leaves in the panicle differentiation stage are the most distinguishable, and it is easy to diagnose and identify nitrogen nutrition, which can be used as an effective period and location for nitrogen nutrition diagnosis. The components of six color space, RGB and HSI,can best reflect nitrogen nutrition status. Regarding the recognition effect, the BP neural network is higher than the probabilistic neural network, and its overall recognition accuracy is 90%.
【Objectives】Identifying key indicators for image and establishing models for processing of massive image data rapidly were two main steps in achieving nitrogen nutrition diagnosis. This project screened the sensitive period and location of rice nitrogen nutrition diagnosis, optimized the image processing technical parameters, and compared the reliability between two methods, BP neural network and probabilistic neural network, in nutrient diagnosis, which provided ideas and methods for determining the nutritional status of crops and inverting the growth process quickly and accurately by using computer vision virtual technology.【Methods】In this study, super hybrid rice ‘LYP9’ was used as experimental crop to set up four kinds of rice cultivation experiments at different fertilization levels(equivalent to N 0, 210, 300 and 390 kg/hm~2), the image data of a total of 1920 groups of the first leaves and the second leaves, the third leaves from crop top, and their corresponding sheaths were obtained by scanning with a scanner during the panicle differentiation stage and the full heading stage. Nineteen rice indexes were obtained. The diagnosis models of rice nitrogen nutrition on standardized nineteen rice characteristic indexes obtained by image processing were respectively established by applying BP neural network and probabilistic neural network. Based on the models, rice nitrogen nutrition diagnosis and identification were carried out. 【Results】1) The accuracy of overall recognition of rice at the panicle differentiation stage was higher than that of the full heading stage; the image data of the third leaves was the most reliable among the three parts of leaves; 2) For the panicle differentiation stage and the full heading stage, the overall recognition accuracy of the nineteen rice characteristic indexes obtained by image processing was higher by using the BP neural network, compared with the probabilistic neural network. The accuracy of diagnosis and identification of rice nitrogen nutrition in the nineteen rice characteristic indexes of the third leaves at the panicle differentiation stage processed through the BP neural network was up to 90%. The accuracy of diagnosis and identification of rice nitrogen nutrition in the nineteen rice characteristic indexes of the second leaves and the third leaves at the panicle differentiation stage processed through the probabilistic neural network was up to 82%. 【Conclusions】The leaf characteristics of the third leaves in the panicle differentiation stage are the most distinguishable, and it is easy to diagnose and identify nitrogen nutrition, which can be used as an effective period and location for nitrogen nutrition diagnosis. The components of six color space, RGB and HSI,can best reflect nitrogen nutrition status. Regarding the recognition effect, the BP neural network is higher than the probabilistic neural network, and its overall recognition accuracy is 90%.
引文
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[3]李明,张长利,房俊龙.基于图像处理技术的小麦叶面积指数的提取[J].农业工程学报,2010, 26(1):205-209.Li M, Zhang C L, Fang J L. Extraction of leaf area index of wheat based on image processing technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(1):205-209.
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[5]张楠楠,刘伟,王伟,等.玉米霉变及黄曲霉毒素的图像处理检测方法[J].中国粮油学报,2014, 29(2):82-88.Zhang N N, Liu W, Wang W, et al. Image processing method of corn kernels mildew infection and Aflatoxin levels[J]. Journal of the Chinese Cereals and Oils Association, 2014, 29(2):82-88.
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[12]刘江桓.基于机器视觉技术的水稻营养快速诊断研究[D].南昌:江西农业大学硕士学位论文,2011.Liu J H. Research of paddy rice nutrition rapid diagnosis based on computer vision technology[D]. Nanchang:MS Thesis of Jiangxi Agricultural University, 2011.
[13]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.Go 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.
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